# Learning with less data via Weakly Labeled Patch Classification in Digital Pathology

arXiv: https://arxiv.org/abs/1911.12425 · HTML: https://mlsanity.com/paper/1911.12425/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning](https://mlsanity.com/paper/2202.09059/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey](https://mlsanity.com/paper/2201.09267/) (2022)
- [Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification](https://mlsanity.com/paper/2107.05940/) (2021)
- [Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images](https://mlsanity.com/paper/2201.02627/) (2022)
- [Understanding the impact of image and input resolution on deep digital pathology patch classifiers](https://mlsanity.com/paper/2204.13829/) (2022)
- [Attention-based Dynamic Subspace Learners for Medical Image Analysis](https://mlsanity.com/paper/2206.09068/) (2022)
- [Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures](https://mlsanity.com/paper/2404.06080/) (2024)

## Abstract (arXiv)

In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations. We address this issue by learning transferable features from weakly labeled data, which are collected from various parts of the body and are organized by non-medical experts. In this paper, we show that features learned from such weakly labeled datasets are indeed transferable and allow us to achieve highly competitive patch classification results on the colorectal cancer (CRC) dataset [1] and the PatchCamelyon (PCam) dataset [2] while using an order of magnitude less labeled data.