Paper: http://ceur-ws.org/Vol-2882/paper31.pdf Syed Muhammad Faraz Ali, Muhammad Taha Khan, Syed Unaiz Haider, Talha Ahmed, Zeshan Khan and Muhammad Atif Tahir : Depth-wise Separable Atrous Convolution for Polyps Segmentation in Gastro-Intestinal Tract. Proc. of MediaEval 2020, 14-15 December 2020, Online. Identification of polyps in endoscopic images is critical for the diagnosis of colon cancer. Finding the exact shape and size of polyps requires the segmentation of endoscopic images. This research explores the advantage of using depth-wise separable convolution in the atrous convolution of the ResUNet++ architecture. Deep atrous spatial pyramid pooling was also implemented on the ResUNet++ architecture. The results show that architecture with separable convolution has a smaller size and fewer GFLOPs without degrading the performance too much.

1. Medico
Faraz et al
About
Approach
Architecture
Results
Conclusion
Depth-wise Separable Atrous Convolution for
Polyps Segmentation in Gastro-Intestinal Tract
Syed Muhammad Faraz Ali, Muhammad Taha Khan, Syed
Unaiz Haider, Talha Ahmed, Zeshan Khan, Muhammad Atif
Tahir
National University of Computer and Emerging Sciences
MediaEval’20, Medico Depth-wise Separable Atrous Convolution for Segmentation 1 / 6

2. Medico
Faraz et al
About
Approach
Architecture
Results
Conclusion
About the Challenge
Medico automatic polyp segmentation
Robust and accurate detection of polyps
Detection of polyps with high efficiency
MediaEval’20, Medico Depth-wise Separable Atrous Convolution for Segmentation 2 / 6

3. Medico
Faraz et al
About
Approach
Architecture
Results
Conclusion
Approach
Followed ResUNet++ architecture Fig.1
Replaced atrous convolution bridge of ResUNet++ with depth-wise
separable convolution
Replace ResUNet++ bridge with Fig. 2
Models were trained on Mean Intersection over Union (mIoU) loss
Figure 1: Process Flow
Figure 2: DASPP Module
MediaEval’20, Medico Depth-wise Separable Atrous Convolution for Segmentation 3 / 6

4. Medico
Faraz et al
About
Approach
Architecture
Results
Conclusion
Architecture
Following 3 architectures were
implemented:
sepv conv resunet++ : ASPP
module from ResUNet++ replaced
with depth-wise separable
convolution.
dsapp resunet++ : ASPP module
replaced with DASPP module
dsapp relu resunet++ :
dsapp resunet++ implemented with
ReLu Activation activation.
Learning curve and results were
compared with UNet and
ResUNet++
Figure 3: Learning Curve
MediaEval’20, Medico Depth-wise Separable Atrous Convolution for Segmentation 4 / 6

5. Medico
Faraz et al
About
Approach
Architecture
Results
Conclusion
Results
Model Recall Precision Dice mIoU
Unet 75.23% 84.52% 71.91% 59.53%
resunet++ 64.97% 89.81% 78.35% 69.48%
sepv conv resunet++ 60.55% 93.31% 77.25% 67.56%
dsapp resunet++ 69.72% 82.62% 76.66% 66.71%
dsapp relu resunet++ 61.54% 92.33% 74.63% 66.03%
Table 1: Test Data Results
Model Params GFLOPs
Unet 3,588,997 7,165,148
resunet++ 4,371,265 8,718,068
sepv conv resunet++ 3,047,265 6,070,057
dsapp resunet++ 5,024,705 10,024,898
dsapp relu resunet++ 5,024,705 10,024,898
Table 2: Model Size
MediaEval’20, Medico Depth-wise Separable Atrous Convolution for Segmentation 5 / 6

6. Medico
Faraz et al
About
Approach
Architecture
Results
Conclusion
Conclusion and Future Work
Conclusion
Implementation of depth-wise separable convolution resulted in
smaller model size with comparable performance
Going deep in Atrous bridge does not improve performance and
may result in overfitting
Future Work
Training on larger number of epoch
Hyper parameter tuning
Implement depth-wise separable convolution in other module of
fig.1
MediaEval’20, Medico Depth-wise Separable Atrous Convolution for Segmentation 6 / 6