# Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes

arXiv: https://arxiv.org/abs/2401.01164 · HTML: https://mlsanity.com/paper/2401.01164/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes](https://mlsanity.com/paper/1912.06842/) (2019)
- [Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers](https://mlsanity.com/paper/2109.05819/) (2021)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism](https://mlsanity.com/paper/2208.00617/) (2022)

## Built on by (most cited first, 1 of 1)

- [Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes](https://mlsanity.com/paper/2406.19814/) (2024)

## Abstract (arXiv)

Multi-class colorectal tissue classification is a challenging problem that is typically addressed in a setting, where it is assumed that ample amounts of training data is available. However, manual annotation of fine-grained colorectal tissue samples of multiple classes, especially the rare ones like stromal tumor and anal cancer is laborious and expensive. To address this, we propose a knowledge distillation-based approach, named KD-CTCNet, that effectively captures local texture information from few tissue samples, through a distillation loss, to improve the standard CNN features. The resulting enriched feature representation achieves improved classification performance specifically in low data regimes. Extensive experiments on two public datasets of colorectal tissues reveal the merits of the proposed contributions, with a consistent gain achieved over different approaches across low data settings. The code and models are publicly available on GitHub.