# Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv: https://arxiv.org/abs/2408.04940 · HTML: https://mlsanity.com/paper/2408.04940/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 42 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Built on by (most cited first, 16 of 16)

- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding](https://mlsanity.com/paper/2601.08758/) (2026)
- [Capsule Endoscopy Multi-classification via Gated Attention and Wavelet Transformations](https://mlsanity.com/paper/2410.19363/) (2024)
- [Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal Endoscopy](https://mlsanity.com/paper/2410.21302/) (2024)
- [CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy](https://mlsanity.com/paper/2410.20231/) (2024)
- [CASCRNet: An Atrous Spatial Pyramid Pooling and Shared Channel Residual based Network for Capsule Endoscopy](https://mlsanity.com/paper/2410.17863/) (2024)
- [Integrating Deep Feature Extraction and Hybrid ResNet-DenseNet Model for Multi-Class Abnormality Detection in Endoscopic Images](https://mlsanity.com/paper/2410.18457/) (2024)
- [CapsoNet: A CNN-Transformer Ensemble for Multi-Class Abnormality Detection in Video Capsule Endoscopy](https://mlsanity.com/paper/2410.18879/) (2024)
- [CapsuleNet: A Deep Learning Model To Classify GI Diseases Using EfficientNet-b7](https://mlsanity.com/paper/2410.19151/) (2024)
- [A Multimodal Approach For Endoscopic VCE Image Classification Using BiomedCLIP-PubMedBERT](https://mlsanity.com/paper/2410.19944/) (2024)
- [Exploring Self-Supervised Learning with U-Net Masked Autoencoders and EfficientNet-B7 for Improved Gastrointestinal Abnormality Classification in Video Capsule Endoscopy](https://mlsanity.com/paper/2410.19899/) (2024)
- [Multi-Class Abnormality Classification Task in Video Capsule Endoscopy](https://mlsanity.com/paper/2410.19973/) (2024)
- [Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy](https://mlsanity.com/paper/2411.01479/) (2024)
- [FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification](https://mlsanity.com/paper/2411.02637/) (2024)
- [EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification](https://mlsanity.com/paper/2510.17200/) (2025)
- [Benchmarking the Domain Gap: Model Selection Instability Under Domain Shift in Video Capsule Endoscopy](https://mlsanity.com/paper/2607.22736/) (2026)

## Abstract (arXiv)

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers.