Reliable Mislabel Detection for Video Capsule Endoscopy Data
🤖 AI Summary
Medical image annotation relies heavily on expert input, which often introduces label noise and ambiguous boundaries that hinder the performance of deep learning models. Addressing this challenge in the context of video capsule endoscopy data, this work proposes the first systematic framework for mislabel detection and cleansing, integrating deep learning with a verification mechanism involving three board-certified gastroenterologists to automatically identify and correct erroneously annotated samples. The proposed approach significantly improves anomaly detection performance, outperforming existing baselines and demonstrating the efficacy and necessity of high-fidelity mislabel correction in medical video analysis.
Application Category
📝 Abstract
The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets. In medical imaging, however, obtaining such datasets is particularly challenging since annotations must be provided by specialized physicians, which severely limits the pool of annotators. Furthermore, class boundaries can often be ambiguous or difficult to define which further complicates machine learning-based classification. In this paper, we want to address this problem and introduce a framework for mislabel detection in medical datasets. This is validated on the two largest, publicly available datasets for Video Capsule Endoscopy, an important imaging procedure for examining the gastrointestinal tract based on a video stream of lowresolution images. In addition, potentially mislabeled samples identified by our pipeline were reviewed and re-annotated by three experienced gastroenterologists. Our results show that the proposed framework successfully detects incorrectly labeled data and results in an improved anomaly detection performance after cleaning the datasets compared to current baselines.
Problem
Research questions and friction points this paper is trying to address.
mislabel detection
medical imaging
Video Capsule Endoscopy
data annotation
label noise
Innovation
Methods, ideas, or system contributions that make the work stand out.
mislabel detection
video capsule endoscopy
medical image annotation
anomaly detection
data cleaning
🔎 Similar Papers
No similar papers found.
