# Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology

arXiv: https://arxiv.org/abs/2102.09963 · HTML: https://mlsanity.com/paper/2102.09963/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia](https://mlsanity.com/paper/1805.00632/) (2018)

## Built on by (most cited first, 4 of 4)

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domain](https://mlsanity.com/paper/2305.14979/) (2023)
- [SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis](https://mlsanity.com/paper/2606.22144/) (2026)
- [Immunofluorescence Capillary Imaging Segmentation: Cases Study](https://mlsanity.com/paper/2207.06861/) (2022)

## Abstract (arXiv)

Purpose. Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We build a computer-assisted detection (CADe) system that can classify still images or video frames as normal or abnormal with high diagnostic accuracy. Methods. We present a new benchmark dataset containing 68K binary labeled frames extracted from 114 patient videos whose imaged areas have been resected and correlated to histopathology. Our novel convolutional network (CNN) architecture solves the binary classification task and explains what features of the input domain drive the decision-making process of the network. Results. The proposed method achieved an average accuracy of 91.7 % compared to the 94.7 % achieved by a group of 12 senior clinicians. Our novel network architecture produces deeply supervised activation heatmaps that suggest the network is looking at intrapapillary capillary loop (IPCL) patterns when predicting abnormality. Conclusion. We believe that this dataset and baseline method may serve as a reference for future benchmarks on both video frame classification and explainability in the context of ESCN detection. A future work path of high clinical relevance is the extension of the classification to ESCN types.