# Federated Learning for the Classification of Tumor Infiltrating Lymphocytes

arXiv: https://arxiv.org/abs/2203.16622 · HTML: https://mlsanity.com/paper/2203.16622/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [Multi-task Federated Learning for Heterogeneous Pancreas Segmentation](https://mlsanity.com/paper/2108.08537/) (2021)
- [Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types](https://mlsanity.com/paper/1907.03960/) (2019)

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

- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [Evaluating histopathology transfer learning with ChampKit](https://mlsanity.com/paper/2206.06862/) (2022)
- [Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading](https://mlsanity.com/paper/2302.06089/) (2023)
- [GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging](https://mlsanity.com/paper/2410.00173/) (2024)

## Abstract (arXiv)

We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as the example use case, on quantifiying the distribution of tumor infiltrating lymphocytes within whole slide images (WSIs). A deep learning classification model was trained using 50*50 square micron patches extracted from the WSIs. We simulated a FL environment in which a dataset, generated from WSIs of cancer from numerous anatomical sites available by The Cancer Genome Atlas repository, is partitioned in 8 different nodes. Our results show that the model trained with the federated training approach achieves similar performance, both quantitatively and qualitatively, to that of a model trained with all the training data pooled at a centralized location. Our study shows that FL has tremendous potential for enabling development of more robust and accurate models for histopathology image analysis without having to collect large and diverse training data at a single location.