Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps

TL;DR

This paper improves the accuracy and interpretability of CNN models for colorectal polyp segmentation in medical images by evaluating uncertainty estimation methods and enhancing FCN architectures, achieving a new state-of-the-art performance.

Contribution

The paper develops and compares advanced uncertainty estimation and interpretability techniques in CNNs for medical image segmentation, improving accuracy and transparency.

Findings

Achieved 76.06% mean IOU accuracy on EndoScene dataset.

Enhanced FCN architectures outperform previous models.

Provided insights into model uncertainty and interpretability in medical segmentation.

Abstract

Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. We evaluate and enhance several architectures of Fully Convolutional Networks (FCNs) for semantic segmentation of colorectal polyps and provide a comparison between these models. Our highest performing model achieves a 76.06\% mean IOU accuracy on the EndoScene dataset, a considerable improvement over the previous state-of-the-art.