First part shows several methods to sample points from arbitrary distributions. Second part shows application to population genetics to infer population size and divergence time using obtained sequence data.
First part shows several methods to sample points from arbitrary distributions. Second part shows application to population genetics to infer population size and divergence time using obtained sequence data.
Lec14: Evaluation Framework for Medical Image SegmentationUlaş Bağcı
How to evaluate accuracy of image segmentation?
– Gold standard ~ surrogate of truths
– Qualitative • Visual
• Inter-andintra-observeragreementrates – Quantitative
• Volumetricmeasurements(regression) • Regionoverlaps
• Shapebasedmeasurements
• Theoreticalcomparisons
• STAPLE,Uncertaintyguidance,andevaluationw/otruths
Clustering – K-means – FCM (fuzzyc-means) – SMC (simple membership based clustering) – AP(affinity propagation) – FLAB(fuzzy locally adaptive Bayesian) – Spectral Clustering Methods ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Pattern recognition and Machine Learning.Rohit Kumar
Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.
The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
This document summarizes a presentation about variational autoencoders (VAEs) presented at the ICLR 2016 conference. The document discusses 5 VAE-related papers presented at ICLR 2016, including Importance Weighted Autoencoders, The Variational Fair Autoencoder, Generating Images from Captions with Attention, Variational Gaussian Process, and Variationally Auto-Encoded Deep Gaussian Processes. It also provides background on variational inference and VAEs, explaining how VAEs use neural networks to model probability distributions and maximize a lower bound on the log likelihood.
This document provides an overview of metaheuristics, which are high-level problem-solving techniques for optimization problems. It begins with the history and definition of metaheuristics, then discusses their main characteristics such as neighborhood structures and intensification/diversification. Various metaheuristic methods are classified and examples are given, such as evolutionary algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. Real-world applications are mentioned in areas like scheduling and logistics. Advantages of metaheuristics include their adaptability, while disadvantages are their lack of optimality guarantees and theoretical foundations.
Lec14: Evaluation Framework for Medical Image SegmentationUlaş Bağcı
How to evaluate accuracy of image segmentation?
– Gold standard ~ surrogate of truths
– Qualitative • Visual
• Inter-andintra-observeragreementrates – Quantitative
• Volumetricmeasurements(regression) • Regionoverlaps
• Shapebasedmeasurements
• Theoreticalcomparisons
• STAPLE,Uncertaintyguidance,andevaluationw/otruths
Clustering – K-means – FCM (fuzzyc-means) – SMC (simple membership based clustering) – AP(affinity propagation) – FLAB(fuzzy locally adaptive Bayesian) – Spectral Clustering Methods ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Pattern recognition and Machine Learning.Rohit Kumar
Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.
The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
This document summarizes a presentation about variational autoencoders (VAEs) presented at the ICLR 2016 conference. The document discusses 5 VAE-related papers presented at ICLR 2016, including Importance Weighted Autoencoders, The Variational Fair Autoencoder, Generating Images from Captions with Attention, Variational Gaussian Process, and Variationally Auto-Encoded Deep Gaussian Processes. It also provides background on variational inference and VAEs, explaining how VAEs use neural networks to model probability distributions and maximize a lower bound on the log likelihood.
This document provides an overview of metaheuristics, which are high-level problem-solving techniques for optimization problems. It begins with the history and definition of metaheuristics, then discusses their main characteristics such as neighborhood structures and intensification/diversification. Various metaheuristic methods are classified and examples are given, such as evolutionary algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. Real-world applications are mentioned in areas like scheduling and logistics. Advantages of metaheuristics include their adaptability, while disadvantages are their lack of optimality guarantees and theoretical foundations.