Paper: http://ceur-ws.org/Vol-2882/paper20.pdf YouTube: https://youtu.be/CVelQl5Luf0 Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh and Minh-Triet Tran : HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and UNet for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online. The Medico: Multimedia Task focuses on developing an efficient and accurate framework to computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps in endoscopic images of the gastrointestinal (GI) tract. We are HCMUS-team approach a solution, which includes combination Residual module, Inception module, Adaptive Convolutional neural network with Unet model and PraNet to semantic segmentation all types of polyps in endoscopic images. We submit multiple runs with different architecture and parameters in our model. Our methods show potential results in accuracy and efficiency through multiple experiments.

1. Author: Quoc Huy Trinh, Minh Van Nguyen, Thiet Gia Huynh, Minh Triet Tran
HCMUS-Juniors 2020
at Medico Task in MediaEval 2020:
Refined Deep Neural Network and U-Net
for Polyps Segmentation
DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 1

2. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 2
MENU
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

3. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 3
ABSTRACT
The Medico: Multimedia Task 2020 focuses on developing an efficient and accurate computer-aided
diagnosis system for automatic segmentation. We participate in task 1, polyps segmentation task, which is
to develop algorithms for segmenting polyps on a comprehensive dataset. In this task, we propose methods
combining Residual module, Inception module, Adaptive Convolutional neural network with U-Net model
and PraNet for semantic segmentation of various types of polyps in endoscopic images. We select 5 runs
with different architecture and parameters in our methods. Our methods show potential results in accuracy
and efficiency through multiple experiments.

4. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 4
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

5. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 5
CHALLENGE DESCRIPTION
The “Medico automatic polyp segmentation challenge” aims to develop computer-aided diagnosis systems
for automatic polyp segmentation to detect all types of polyps (for example, irregular polyp, smaller or flat
polyps) with high efficiency and accuracy. The main goal of the challenge is to benchmark semantic
segmentation algorithms on a publicly available dataset, emphasizing robustness, speed, and generalization.
Participants will get access to a dataset consisting of 1,000 segmented polyp images from the
gastrointestinal tract and a separate testing dataset. The challenge consists of two mandatory tasks, each
focused on a different requirement for efficient polyp detection. We hope that this task encourages
multimedia researchers to apply their vast knowledge to the medical field and make an impact that may
affect real live

6. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 6
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

7. DATASET
The dataset contains 1,000 polyp images and their corresponding ground truth mask. The datasets were collected
from real routine clinical examinations at Vestre Viken Health Trust (VV) in Norway by expert
gastroenterologists. The VV is the collaboration of the four hospitals that provide healthcare service to 470,000
peoples
DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 7
POLYPS IMAGE POLYPS MASK

8. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 8
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

9. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 9
METHOD
- U-Net: Using the simple U-Net
- Leaky ReLU + U-Net: Combine Leaky ReLU in convolution block with the U-Net
- Inception Modules + U-Net: Combine Inception modules in convolution block with
the U-Net
- ResUNet: Combine Residual blocks of convolutional layers
- PraNet: a parallel reverse attention network

10. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION
10

11. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
RESULT: Our team results after 5 runs
RUN JACCARD DSC RECALL PRECISION ACCURACY F2
1 0.322841318 0.434127614 0.552935567 0.408340977 0.861768135 0.483433644
2 0.290599856 0.41149787 0.765371714 0.329960348 0.739005324 0.524980002
3 0.405763698 0.514761924 0.507163264 0.757444359 0.901111603 0.500731409
4 0.294509379 0.418891573 0.764476695 0.340514156 0.75517951 0.534777353
5 0.765977082 0.840506183 0.894394614 0.844555946 0.946558279 0.857688914
11

12. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION
12

13. DISSCUSSION
DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
When we use PraNet to test on Medical Images, it performs better than on the camouflage experiment. While PraNet
gets high accuracy, but the architecture requires a well-qualified setting to run the model.
13

14. DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
THANKS FOR WATCHING
14