# Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

arXiv: https://arxiv.org/abs/2303.14845 · HTML: https://mlsanity.com/paper/2303.14845/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 26 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [Orderless Recurrent Models for Multi-label Classification](https://mlsanity.com/paper/1911.09996/) (2019)
- [Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading](https://mlsanity.com/paper/2203.04013/) (2022)

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

- [Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics](https://mlsanity.com/paper/2404.12973/) (2024)
- [Unleashing the Power of Image-Tabular Self-Supervised Learning via Breaking Cross-Tabular Barriers](https://mlsanity.com/paper/2512.14026/) (2025)
- [Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal Learning](https://mlsanity.com/paper/2406.13979/) (2024)
- [Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis](https://mlsanity.com/paper/2401.01553/) (2024)
- [Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading](https://mlsanity.com/paper/2408.08527/) (2024)
- [Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction](https://mlsanity.com/paper/2406.06517/) (2024)
- [Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization](https://mlsanity.com/paper/2508.16479/) (2025)
- [Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling](https://mlsanity.com/paper/2505.10729/) (2025)
- [Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncology](https://mlsanity.com/paper/2406.07078/) (2024)
- [Joint Modelling Histology and Molecular Markers for Cancer Classification](https://mlsanity.com/paper/2502.07979/) (2025)
- [Interpretable Multimodal Cancer Prototyping with Whole Slide Images and Incompletely Paired Genomics](https://mlsanity.com/paper/2511.21937/) (2025)
- [FOD-Diff: 3D Multi-Channel Patch Diffusion Model for Fiber Orientation Distribution](https://mlsanity.com/paper/2512.16075/) (2025)

## Abstract (arXiv)

Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma,as well as related histology and molecular markers on a multi-institutional dataset.