Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation

@article{Ahmed2021ExplainableMI,
  title={Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation},
  author={Awadelrahman M. A. Ahmed and Leen A. M. Ali},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.01665}
}
This paper contributes in automating medical image segmentation by proposing generative adversarial network based models to segment both polyps and instruments in endoscopy images. A main contribution of this paper is providing explanations for the predictions using layer-wise relevance propagation approach, showing which pixels in the input image are more relevant to the predictions. The models achieved 0.46 and 0.70, on Jaccard index and 0.84 and 0.96 accuracy, on the polyp segmentation and… 

Figures and Tables from this paper

References

SHOWING 1-8 OF 8 REFERENCES

Kvasir-SEG: A Segmented Polyp Dataset

TLDR
This paper presents Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist, and demonstrates the use of the dataset with a traditional segmentation approach and a modern deep-learning based Convolutional Neural Network approach.

MedAI: Transparency in Medical Image Segmentation

TLDR
This work proposes three tasks to meet specific gastrointestinal image segmentation challenges collected from experts within the field, including two separate segmentation scenarios and one scenario on transparent ML systems.

Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy

TLDR
Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements.

Image-to-Image Translation with Conditional Adversarial Networks

TLDR
Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

TLDR
This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers by introducing a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks.

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

TLDR
This introductory paper presents recent developments and applications in the deep learning field and makes a plea for a wider use of explainable learning algorithms in many applications.

Layer-Wise Relevance Propagation: An Overview

TLDR
This chapter gives a concise introduction to LRP with a discussion of how to implement propagation rules easily and efficiently, how the propagation procedure can be theoretically justified as a ‘deep Taylor decomposition’, how to choose the propagation rules at each layer to deliver high explanation quality, and how LRP can be extended to handle a variety of machine learning scenarios beyond deep neural networks.

Kvasir-seg: A segmented polyp

  • dataset. International Conference on Multimedia Modeling
  • 2020