Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps
This work addresses the need for reliable and interpretable AI models in medical image analysis, specifically for colorectal polyp segmentation, but it appears incremental as it evaluates and enhances existing architectures.
The paper tackled the problem of improving precision, interpretability, and uncertainty estimation in convolutional neural networks for semantic segmentation of colorectal polyps from colonoscopy images, achieving a 76.06% mean IOU accuracy on the EndoScene dataset, which is a considerable improvement over the previous state-of-the-art.
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.