Abstract
Video Capsule Endoscopy has become an indispensable diagnostic tool for gastrointestinal disorders due to its non-invasive nature and ability to capture high-resolution images of the small intestine. However, the enormous volume of data generated during a single procedure makes manual inspection labor-intensive, time-consuming, and prone to inter-observer variability. In this work, we propose a novel framework that combines self-supervised learning through a U-Net-based masked autoencoder with supervised feature extraction using EfficientNet-B7 for multi-class abnormality classification in Endoscopy images. The U-Net model is first trained in a self-supervised manner using Gaussian noise removal and masked reconstruction to learn robust visual representations without requiring annotations. The learned encoder features are then fused with EfficientNet-B7 features to form a rich, discriminative representation for classification. We evaluate our approach on the Capsule Vision 2024 Challenge dataset consisting of ten abnormality classes and a dominant normal class. Experimental results demonstrate that the proposed fusion framework achieves a macro-F1 of 0.842 and balanced accuracy of 0.940, outperforming frozen Self-Supervised Learning ViT baselines including EndoViT and DINOv2. The study highlights the effectiveness of task-specific self-supervised representation learning and feature fusion in addressing class imbalance and improving diagnostic accuracy in real-world medical imaging scenarios.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/SafeSurg_010.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=nnZJnIGze4
BibTex
@InProceedings{KanVam_SelfSupervised_MICCAISAT2026,
author = { Kancharla, Vamshi Krishna AND Raju, Dasari Naga AND Kumar, Kaveti Pavan},
title = { { Self-Supervised Denoising Reconstruction Outperforms Foundation Models for Capsule Endoscopy Abnormality Classification } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS pending},
month = {pending},
page = {pending}
}