# Multi-task pre-training of deep neural networks for digital pathology

arXiv: https://arxiv.org/abs/2005.02561 · HTML: https://mlsanity.com/paper/2005.02561/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 14 of 14)

- [Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles](https://mlsanity.com/paper/2103.05121/) (2021)
- [Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model](https://mlsanity.com/paper/2311.09847/) (2023)
- [MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs](https://mlsanity.com/paper/2312.03731/) (2023)
- [One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification](https://mlsanity.com/paper/2203.00077/) (2022)
- [Rotation-Agnostic Image Representation Learning for Digital Pathology](https://mlsanity.com/paper/2311.08359/) (2023)
- [Tissue Concepts: supervised foundation models in computational pathology](https://mlsanity.com/paper/2409.03519/) (2024)
- [HistoTransfer: Understanding Transfer Learning for Histopathology](https://mlsanity.com/paper/2106.07068/) (2021)
- [Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images](https://mlsanity.com/paper/2309.07394/) (2023)
- [LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset](https://mlsanity.com/paper/2301.06304/) (2023)
- [FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification](https://mlsanity.com/paper/2407.15312/) (2024)
- [Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology](https://mlsanity.com/paper/2403.03891/) (2024)
- [UniCell: Universal Cell Nucleus Classification via Prompt Learning](https://mlsanity.com/paper/2402.12938/) (2024)
- [Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review](https://mlsanity.com/paper/2410.19820/) (2024)
- [Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study](https://mlsanity.com/paper/2309.17223/) (2023)

## Abstract (arXiv)

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.