U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
U-Net is a convolutional neural network (CNN) architecture designed for semantic segmentation tasks, especially in the field of medical image analysis. It was introduced by Olaf Ronneberger, Philipp Fischer, and Thomas Brox in 2015. The name "U-Net" comes from its U-shaped architecture.
Key features of the U-Net architecture:
U-Shaped Design: U-Net consists of a contracting path (downsampling) and an expansive path (upsampling). The architecture resembles the letter "U" when visualized.
Contracting Path (Encoder):
The contracting path involves a series of convolutional and pooling layers.
Each convolutional layer is followed by a rectified linear unit (ReLU) activation function and possibly other normalization or activation functions.
Pooling layers (usually max pooling) reduce spatial dimensions, capturing high-level features.
Expansive Path (Decoder):
The expansive path involves a series of upsampling and convolutional layers.
Upsampling is achieved using transposed convolution (also known as deconvolution or convolutional transpose).
Skip connections are established between corresponding layers in the contracting and expansive paths. These connections help retain fine-grained spatial information during the upsampling process.
Skip Connections:
Skip connections concatenate feature maps from the contracting path to the corresponding layers in the expansive path.
These connections facilitate the fusion of low-level and high-level features, aiding in precise localization.
Final Layer:
The final layer typically uses a convolutional layer with a softmax activation function for multi-class segmentation tasks, providing probability scores for each class.
U-Net's architecture and skip connections help address the challenge of segmenting objects with varying sizes and shapes, which is often encountered in medical image analysis. Its success in this domain has led to its application in other areas of computer vision as well.
The U-Net architecture has also been extended and modified in various ways, leading to improvements like the U-Net++ architecture and variations with attention mechanisms, which further enhance the segmentation performance.
U-Net's intuitive design and effectiveness in semantic segmentation tasks have made it a cornerstone in the field of medical image analysis and an influential architecture for researchers working on segmentation challenges.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
Swin Transformer가 최근 오브젝트디텍션 그리고 Semantic segmentation분야에서의 성능이 가장 좋은 모델 중 하나로
주목 받고 있습니다.
Swin Transformer nlp분야에서 많이 쓰이는 트랜스포머를 비전 분야에 적용한 모델로 Hierarchical feature maps과
Window-based Self-attention의 특징적입니다 Swin Transformer는 작년 구글에서 제안된 방법인
비전 트랜스포머의 한계점을 개선한 모델이라고 보시면 됩니다
트랜스포머의 한계란.. ㄷㄷ 이네요
이미지 처리팀의 김선옥님이 자세한 리뷰 도와주셨습니다!!
오늘도 많은 관심 미리 감사드립니다!!
https://youtu.be/L3sH9tjkvKI
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Image Segmentation: Approaches and ChallengesApache MXNet
This slides go over the problem of deep semantic segmentation. It covers the different approaches taken, from hourglass autoencoder to pyramid networks.
Slides by Thomas Delteil
Comparison between JPEG(DCT) and JPEG 2000(DWT) compression standardsRishab2612
This topic comes under the Image Processing.In this comparison between JPEG and JPEG 2000 compression standard techniques is made.The PPT comprises of results, analysis and conclusion along with the relevant outputs
CNN has become an important part of deep learning networks. It has become crucial to get the in-depth knowledge on CNN. This ppt gives a brief overview on CNN.
Occlusion and Abandoned Object Detection for Surveillance ApplicationsEditor IJCATR
Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects
and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries
could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded
object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in
video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled
by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of
Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and
tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several
experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and
effectiveness of the proposed method.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...Joonhyung Lee
A presentation introducting DeepLab V3+, the state-of-the-art architecture for semantic segmentation. It also includes detailed descriptions of how 2D multi-channel convolutions function, as well as giving a detailed explanation of depth-wise separable convolutions.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Swin Transformer가 최근 오브젝트디텍션 그리고 Semantic segmentation분야에서의 성능이 가장 좋은 모델 중 하나로
주목 받고 있습니다.
Swin Transformer nlp분야에서 많이 쓰이는 트랜스포머를 비전 분야에 적용한 모델로 Hierarchical feature maps과
Window-based Self-attention의 특징적입니다 Swin Transformer는 작년 구글에서 제안된 방법인
비전 트랜스포머의 한계점을 개선한 모델이라고 보시면 됩니다
트랜스포머의 한계란.. ㄷㄷ 이네요
이미지 처리팀의 김선옥님이 자세한 리뷰 도와주셨습니다!!
오늘도 많은 관심 미리 감사드립니다!!
https://youtu.be/L3sH9tjkvKI
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Image Segmentation: Approaches and ChallengesApache MXNet
This slides go over the problem of deep semantic segmentation. It covers the different approaches taken, from hourglass autoencoder to pyramid networks.
Slides by Thomas Delteil
Comparison between JPEG(DCT) and JPEG 2000(DWT) compression standardsRishab2612
This topic comes under the Image Processing.In this comparison between JPEG and JPEG 2000 compression standard techniques is made.The PPT comprises of results, analysis and conclusion along with the relevant outputs
CNN has become an important part of deep learning networks. It has become crucial to get the in-depth knowledge on CNN. This ppt gives a brief overview on CNN.
Occlusion and Abandoned Object Detection for Surveillance ApplicationsEditor IJCATR
Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects
and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries
could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded
object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in
video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled
by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of
Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and
tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several
experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and
effectiveness of the proposed method.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
DeepLab V3+: Encoder-Decoder with Atrous Separable Convolution for Semantic I...Joonhyung Lee
A presentation introducting DeepLab V3+, the state-of-the-art architecture for semantic segmentation. It also includes detailed descriptions of how 2D multi-channel convolutions function, as well as giving a detailed explanation of depth-wise separable convolutions.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Deep learning for image super resolutionPrudhvi Raj
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
Deep learning for image super resolutionPrudhvi Raj
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
Presentation of few recent papers on Deep Learning ... in particular Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Song Han, Huizi Mao, William J. Dally International Conference on Learning Representations ICLR2016
RunPool: A Dynamic Pooling Layer for Convolution Neural NetworkPutra Wanda
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different
classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make training
faster, we used non-saturating neurons and a very efficient GPU implementation
of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry
Here, we have implemented CNN network in FPGA by incorporating a novel technique of convolution which includes pipelining technique as well as parallelism (by optimizing) between the two.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
CONTRAST OF RESNET AND DENSENET BASED ON THE RECOGNITION OF SIMPLE FRUIT DATA...rinzindorjej
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used
Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different
kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer)
are used. The mathematic principle, experiment detail and the experiment result will be explained through
comparison.
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer) are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
1. Ben-Gurion University of the Negev
Deep Learning Image Processing 2018
Eliya Ben Avraham & Laialy Darwesh
U-Net: Convolutional Networks
for
Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox
University of Freiburg, Germany
1
https://arxiv.org/pdf/1505.04597.pdf
2. Introduction
Motivation
Previous work
U-NET architecture
U-NET Training
Data Augmentation
Experiments
Extending U-NET
Conclusion
Topics
2
3. Convolutional Neural Networks (CNN)
Introduction
3
https://www.mathworks.com/videos/introduction-to-deep-learning-what-are-convolutional-neural-networks--1489512765771.html
https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/convolutional_neural_networks.html
The fewer number of connections and weights make convolutional
layers relatively cheap (vs full connect) in terms of memory and
compute power needed.
Convolutional networks make the assumption of locality, and
hence are more powerful
5. Introduction
5
https://www.saagie.com/blog/object-detection-part1
The use of convolutional networks is on classification tasks
where the output to typical image is a single class label.
A class label is supposed to be assigned to each pixel.
In many visual tasks, especially in biomedical image
processing, the desired output should include localization
9. First Task
9
Ciresan, D.C., Gambardella, L.M., Giusti, A., Schmidhuber, J.: Deep neural net- works segment neuronal membranes in electron microscopy images. In: NIPS. pp. 2852{2860 (2012)
Predict the class label of each pixel
Stacks of Electron microscopy (EM) images
EM segmentation challenge at ISBI 2012
30 training images
Training stack Ground truth
Black - neuron membranes
White - cells
10. Second Task
10
ISBI 2015- separation of touching objects of the same class
Light microscopic images (recorded by phase contrast microscopy)
Part of the ISBI cell tracking challenge 2014 and 2015
Raw image
(HeLa cells)
Generated segmentation mask
(white:foreground, black:background)
Ground truth segmentation.
12. Previous work
Ciresan, D.C., Gambardella, L.M., Giusti, A., Schmidhuber, J.: Deep neural net- works segment neuronal membranes in electron microscopy images.
The winner (ISBI 2012) (Ciresan et al.)
Trained a network in a sliding-window (local region (patch) around that pixel)
x Slow because the network must be run separately for each patch
This network can localize
Deep
Neural
Netwok
The training data in terms of patches is much larger than the number of training images
x There is a lot of redundancy
13. Previous work
Ciresan, D.C., Gambardella, L.M., Giusti, A., Schmidhuber, J.: Deep neural net- works segment neuronal membranes in electron microscopy images. In: NIPS. pp. 2852{2860(2012)
The winner (ISBI 2012)
Trade-off between localization accuracy and the use of context.
Larger patches: Require more max-pooling layers → reduce the localization accuracy
Small patches: Allow the network to see only little context
We want a good localization and the use of context at the
same time
Deep
Neural
Netwok
15. Input
image
tile
W - Input volume size
F – Receptive field size (Filter
Size)
P - Zero padding used on the
border
S - Stride
U-NET Architecture
http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html
Output
segmentation map
(here 2 classes)
background and
foreground
Increase the “What”
Reduce the “Where”
Create high-resolution
segmentation map
Output Size (first conv)
= (572 – 3 +2*0)/1 + 1 = 570
→ 570 x 570
Output Size (second
conv)
= (570 – 3 +2*0)/1 + 1 = 568
→ 568 x 568
Concatenation with
high-resolution features
from contracting path
16. U-NET Strategy
Over-tile strategy for arbitrary large images
Segmentation of the yellow area uses input data of the blue area
Raw data extrapolation by mirroring
17. U-net Training
17
𝐸 = −
𝑥∈𝛺
𝑤 𝑥 𝑙𝑜𝑔(pl(x)(x) )
𝑝𝑘(𝑥) = exp 𝑎𝑘 𝑥 /
𝑘′=1
𝐾
exp(𝑎𝑘′ 𝑥 )
Soft-max:
Cross-Entropy loss function:
𝑘- Feature channel
𝑎𝑘(𝑥) - The activation in feature channel k at pixel position x
𝑤(𝑥)- True label per a pixel
18. U-net Training
18
pixel-wise loss weight
Force the network to learn the small separation borders that they
introduce between touching cells.
𝐰 𝒙 = 𝒘𝒄 𝒙 + 𝒘𝟎 𝒆𝒙𝒑 −
𝒅𝟏 𝒙 + 𝒅𝟐 𝒙
𝟐
𝟐𝝈𝟐
𝑤𝑐 𝑥 - weight map to balance the class frequencies
𝑤0 - 10 , 𝜎 ≈ 5 pixels
𝑑1/𝑑2 - Distance to the border of the nearest cell / second nearest cell
Colors :different instances
20. U-net Training
20
Weights initialization
Achieved by Gaussian distribution:
A good initialization of the weights is extremely important
Ideally the initial weights should be adapted such that each feature
map in the network has approximately unit variance)
𝟏 = 𝑽𝒂𝒓
𝒊
𝑵
𝑿𝒊𝑾𝒊
𝝈𝒘 =
𝟏
𝑵
For example: 3x3 convolution and 64 feature channels in the
previous layer 𝑁 = 3 ∗ 3 ∗ 64 = 576
𝝈𝒘 =
𝟐
𝑵
ReLU layers
ReLU unit is zero for non positive inputs
21. Experiments: First task
21
The results of u-net is better than the sliding window convolutional
network which was the best one in 2012 until 2015.
Raw image Ground truth
EM segmentation challenge (since ISBI 2012)
23. Extending U-NET Architecture
23
Application scenarios for volumetric segmentation with the 3D u-net
Semi-automated segmentation
https://arxiv.org/abs/1606.06650
The user annotates some slices of each volume to be segmented
The network predicts the dense segmentation
Fully automated segmentation
Trained with annotated slices
Run on non-annotated volumes
24. Extending U-NET Architecture
24
Voxel size of 1.76×1.76×2.04µm3
Batch normalization (“BN”) before each ReLU
3 × 3 × 3 convolutions, 2 × 2 × 2 max pooling, upconvolution of 2 × 2 × 2
https://arxiv.org/abs/1606.06650
Input: 132 × 132 × 116 voxel tile
Output: 44×44×28 voxel
Application scenarios for volumetric segmentation with the 3D u-net
Jun 2016
25. Extends the previous u-net
25
Additional reconstruction layer
LS is the softmax loss (standard cross entropy loss averaged over all pixels),
LR is the reconstruction loss (standard mean squared error)
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7813160
shifted sigmoid
K = 50 was found to be sufficient
to ensure pre-training convergence
Unsupervised Pre-training for Fully Convolutional Neural Networks
(2016)
26. Summary and Conclusion
26
U-net advantages
Flexible and can be used for any rational image masking task
High accuracy (given proper training, dataset, and training time)
Doesn’t contain any fully connected layers
Faster than the sliding-window (1-sec per image)
Proven to be very powerful segmentation tool in scenarios with limited data
Succeeds to achieve very good performances on different biomedical
segmentation applications.
U-net disadvantages
Larger images need high GPU memory.
Takes significant amount of time to train (relatively many layers)
Pre-trained models not widely available (it's too task specific)
In the last layer there are 2 channels (1 for background and one for foreground)
Left: the training stack (one slice shown). Right: corresponding ground truth; black lines denote neuron membranes. Note complexity of image appearance.
Fig. 3. HeLa cells on glass recorded with DIC (dierential interference contrast) mi-
croscopy. (a) raw image. (b) overlay with ground truth segmentation. Dierent colors
indicate dierent instances of the HeLa cells. (c) generated segmentation mask (white:
foreground, black: background). (d) map with a pixel-wise loss weight to force the
network to learn the border pixels.
Fig. 3. HeLa cells on glass recorded with DIC (dierential interference contrast) mi-
croscopy. (a) raw image. (b) overlay with ground truth segmentation. Dierent colors
indicate dierent instances of the HeLa cells. (c) generated segmentation mask (white:
foreground, black: background). (d) map with a pixel-wise loss weight to force the
network to learn the border pixels.
IEEE International Symposium on Biomedical Imaging (ISBI)
IEEE International Symposium on Biomedical Imaging (ISBI)
In the last layer there are 2 channels (1 for background and one for foreground)
In the last layer there are 2 channels (1 for background and one for foreground)
In the last layer there are 2 channels (1 for background and one for foreground)
Example: if 3*3 convolution and 64 feature channels in the previous layer
then N = 9.64=576