# Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

arXiv: https://arxiv.org/abs/2004.07041 · HTML: https://mlsanity.com/paper/2004.07041/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles](https://mlsanity.com/paper/2103.05121/) (2021)
- [One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification](https://mlsanity.com/paper/2203.00077/) (2022)
- [DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer](https://mlsanity.com/paper/2107.09405/) (2021)
- [Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images](https://mlsanity.com/paper/2308.01982/) (2023)
- [Tissue Concepts: supervised foundation models in computational pathology](https://mlsanity.com/paper/2409.03519/) (2024)
- [VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis](https://mlsanity.com/paper/2410.08397/) (2024)
- [WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression](https://mlsanity.com/paper/2503.18074/) (2025)
- [Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework](https://mlsanity.com/paper/2107.13943/) (2021)
- [Patch Gradient Descent: Training Neural Networks on Very Large Images](https://mlsanity.com/paper/2301.13817/) (2023)
- [Joint multi-task learning improves weakly-supervised biomarker prediction in computational pathology](https://mlsanity.com/paper/2403.03891/) (2024)
- [Evaluating Generic Auto-ML Tools for Computational Pathology](https://mlsanity.com/paper/2112.03622/) (2021)
- [UniCell: Universal Cell Nucleus Classification via Prompt Learning](https://mlsanity.com/paper/2402.12938/) (2024)

## Abstract (arXiv)

We focus on the problem of training convolutional neural networks on gigapixel histopathology images to predict image-level targets. For this purpose, we extend Neural Image Compression (NIC), an image compression framework that reduces the dimensionality of these images using an encoder network trained unsupervisedly. We propose to train this encoder using supervised multitask learning (MTL) instead. We applied the proposed MTL NIC to two histopathology datasets and three tasks. First, we obtained state-of-the-art results in the Tumor Proliferation Assessment Challenge of 2016 (TUPAC16). Second, we successfully classified histopathological growth patterns in images with colorectal liver metastasis (CLM). Third, we predicted patient risk of death by learning directly from overall survival in the same CLM data. Our experimental results suggest that the representations learned by the MTL objective are: (1) highly specific, due to the supervised training signal, and (2) transferable, since the same features perform well across different tasks. Additionally, we trained multiple encoders with different training objectives, e.g. unsupervised and variants of MTL, and observed a positive correlation between the number of tasks in MTL and the system performance on the TUPAC16 dataset.