Paper: http://ceur-ws.org/Vol-2882/paper47.pdf YouTube: https://youtu.be/vMsM4zg2-JY Tien-Phat Nguyen, Tan-Cong Nguyen, Gia-Han Diep, Minh-Quan Le, Hoang-Phuc Nguyen-Dinh, Hai-Dang Nguyen and Minh-Triet Tran : HCMUS at Medico Automatic Polyp Segmentation Task 2020: PraNet and ResUnet++ for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online. The Medico task, MediaEval 2020, explores the challenge of building accurate and high-performance algorithms to detect all types of polyps in endoscopic images. We proposed different approaches leveraging the advantages of either ResUnet++ or PraNet model to efficiently segment polyps in colonoscopy images, with modifications on the network structure, parameters, and training strategies to tackle various observed characteristics of the given dataset. Our methods outperform the other teams' methods, for both accuracy and efficiency. After the evaluation, we are at top 2 for task 1 (with Jaccard index of 0.777, best Precision and Accuracy scores) and top 1 for task 2 (with 67.52 FPS and Jaccard index of 0.658).

1. HCMUS at Medico Automatic Polyp Segmentation Task 2020:
PraNet and ResUnet++ for Polyps Segmentation
Tien-Phat Nguyen, Tan-Cong Nguyen, Gia-Han Diep
Minh-Quan Le, Hoang-Phuc Nguyen-Dinh
Hai-Dang Nguyen, Minh-Triet Tran
MediaEval 2020 Medico Polyp Segmentation Task
{ntphat_student,ntcong, dghan, ndhphuc, lmquan, nhdang}@selab.hcmus.edu.vn, tmtriet@fit.hcmus.edu.vn
Dec.04,2020
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2. Outline
❖ Overview
❖ Approach
❖ Result
❖ Conclusion
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3. Overview
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[1] Debesh Jha et al. “Medico Multimedia Task at MediaEval 2020: Auto-matic Polyp Segmentation”. MediaEval 2020 Workshop. 2020.
MEDICO TASK1
- Segment all types of polyps in colonoscopy images

4. Overview
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TASK 1: Polyp Segmentation
- Data:
- Training set: Kvasir-SEG1
Dataset with 1.000 images
- Test set: 160 images
- Evaluated by Jaccard index
[1] Debesh Jha et al. “Kvasir-seg: A segmented polyp dataset.” In International Conference on Multimedia Modeling. 2020.

5. Overview
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TASK 2: Algorithm efficiency
- Same as Task 1 but focus on the speed of the algorithm
- Participants are required to submit a Docker image
- Evaluated by frames per second rate and Jaccard index

6. - PraNet1
: We use Pranet with Res2net3
backbone as the main
segmentation algorithm.
- Training Signal Annealing2
: We apply TSA strategy to prevent
overfitting in training step.
- Pre/Post-processing methods: We use random rotation and
high-boost filtering for augmentation.
Approach 1: PraNet with Training Signal Annealing
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[1] D.-P. Fan et al. “PraNet: Parallel Reverse Attention Network for Polyp Segmentation”. MICCAI 2020
[2] Xie, Qizhe, et al. “Unsupervised data augmentation for consistency training”. (2019)
[3] Gao, Shanghua, et al. “Res2net: A new multi-scale backbone architecture”. IEEE 2019

7. 7
Training Signal Annealing
- Images with high score (dice-coef) over a threshold are weighted less
than the others.
- Threshold formula:
T : total number of training step
nt
: threshold at step t
K : 2
Approach 1: PraNet with Training Signal Annealing

8. 8
Preprocessing method: High-boost filtering
- Aims to enhance the polyps texture by emphasize high frequency
components.
Approach 1: PraNet with Training Signal Annealing

9. Approach 2: ResUnet++ with Triple-Path and Geodesic Distance
- Base architecture: use ResUnet++1
- Triple-path input: aggregate three
versions of the enhanced input
image
- Guide map: integrate a distance
map layer using Geodesic Distance
Transform2
as a guide mask
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[1] Jha, Debesh, et al. “Resunet++: An advanced architecture for
medical image segmentation.” IEEE 2020
[2] G. Wang et al. “DeepIGeoS: A Deep Interactive Geodesic Framework
for Medical Image Segmentation.IEEE 2019

10. Approach 2: ResUnet++ with Triple-Path and Geodesic Distance
- Create two new enhanced images
using CLAHE and Equalize transform
- Put into separated convolution layers,
then are concatenated together and
passed into a sum convolution layer
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Triple-path input

11. Approach 2: ResUnet++ with Triple-Path and Geodesic Distance
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Guide map with Geodesic Distance
- Pixels closer the boundary should be treated
differently from the pixels inside the polyps
- Calculate the geodesic distance map of the
image based on the center points of the polyps.
- Integrate the distance map to the original
predict layer of the ResUnet++ architecture

12. Result
1. Medico polyp segmentation task 1
- Submitted 5 runs:
➢ Run 1: PraNet with strategy discussed in approach 1
➢ Run 2: Train and ensemble five ResUnet++'s improved model in approach 2
➢ Run 3: Ensemble the results from run 1 and 2.
➢ Run 4: Continue training Run 2 for some epochs with the full dataset that
includes validation set.
➢ Run 5: Single model with highest validation score of approach 2
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13. Result
1. Medico polyp segmentation task 1
- Result:
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Results of the Medico polyp segmentation task 1

14. Result
2. Medico polyp segmentation task 2
- Submitted 2 runs:
➢ Run 1: Choose best model which achieves best validation
result from approach 1
➢ Run 2 : Likewise, choose model with highest validation
score of approach 2
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15. Result
2. Medico polyp segmentation task 2
- Result:
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Results of the Medico polyp segmentation task 2

16. Conclusion
- We proposed different methods leveraging the advantages of
either ResUnet++ or PraNet model to efficiently segment polyps in
colonoscopy images.
- Our suggested approaches show significant improvements in the
results, for both accuracy and efficiency.
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17. Q / A
Thank you!
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