# Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education

arXiv: https://arxiv.org/abs/2307.09426 · HTML: https://mlsanity.com/paper/2307.09426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation](https://mlsanity.com/paper/2305.17370/) (2023)
- [Matching Known Patients to Health Records in Washington State Data](https://mlsanity.com/paper/1307.1370/) (2013)
- [WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval](https://mlsanity.com/paper/2305.03383/) (2023)
- [Towards a privacy-preserving distributed cloud service for preprocessing very large medical images](https://mlsanity.com/paper/2307.06266/) (2023)
- [DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities](https://mlsanity.com/paper/2105.08671/) (2021)

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

- [Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification](https://mlsanity.com/paper/2310.05371/) (2023)

## Abstract (arXiv)

The advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing Artificial Intelligence (AI) algorithms for Digital Pathology (DP). Transferring medical data "as open as possible" enhances the usability of the data for secondary purposes but poses a risk to patient privacy. At the same time, existing regulations push towards keeping medical data "as closed as necessary" to avoid re-identification risks. Generally, these legal regulations require the removal of sensitive data but do not consider the possibility of data linkage attacks due to modern image-matching algorithms. In addition, the lack of standardization in DP makes it harder to establish a single solution for all formats of WSIs. These challenges raise problems for bio-informatics researchers in balancing privacy and progress while developing AI algorithms. This paper explores the legal regulations and terminologies for medical data-sharing. We review existing approaches and highlight challenges from the histopathological perspective. We also present a data-sharing guideline for histological data to foster multidisciplinary research and education.