The document discusses the participation of HCMUS in the MediaEval 2020 Medico Automatic Polyp Segmentation Task, focusing on two main algorithms: Pranet and Resunet++. It describes the methodology used, including data sets, training strategies, and preprocessing techniques, and reports on the performance results from both task 1 and task 2 submissions. The conclusion emphasizes the effectiveness of the proposed methods in improving both accuracy and efficiency in polyp segmentation from colonoscopy images.

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

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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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