# Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading

arXiv: https://arxiv.org/abs/2408.08527 · HTML: https://mlsanity.com/paper/2408.08527/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://mlsanity.com/paper/2106.10270/) (2021)
- [Similarity-Preserving Knowledge Distillation](https://mlsanity.com/paper/1907.09682/) (2019)
- [Learning Deep Representations with Probabilistic Knowledge Transfer](https://mlsanity.com/paper/1803.10837/) (2018)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [Knowledge distillation from multi-modal to mono-modal segmentation networks](https://mlsanity.com/paper/2106.09564/) (2021)
- [CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1909.01068/) (2019)
- [Distilling Holistic Knowledge with Graph Neural Networks](https://mlsanity.com/paper/2108.05507/) (2021)
- [Histographs: Graphs in Histopathology](https://mlsanity.com/paper/1908.05020/) (2019)
- [Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks](https://mlsanity.com/paper/1910.13328/) (2019)
- [Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma](https://mlsanity.com/paper/2303.14845/) (2023)
- [Multi-modal Learning with Missing Modality in Predicting Axillary Lymph Node Metastasis](https://mlsanity.com/paper/2401.01553/) (2024)
- [FOAA: Flattened Outer Arithmetic Attention For Multimodal Tumor Classification](https://mlsanity.com/paper/2403.06339/) (2024)

## Built on by (most cited first, 3 of 3)

- [Disentangled Multi-modal Learning of Histology and Transcriptomics for Cancer Characterization](https://mlsanity.com/paper/2508.16479/) (2025)
- [Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration](https://mlsanity.com/paper/2502.03238/) (2025)
- [Interpretable Multimodal Cancer Prototyping with Whole Slide Images and Incompletely Paired Genomics](https://mlsanity.com/paper/2511.21937/) (2025)

## Abstract (arXiv)

Recently, multimodal deep learning, which integrates histopathology slides and molecular biomarkers, has achieved a promising performance in glioma grading. Despite great progress, due to the intra-modality complexity and inter-modality heterogeneity, existing studies suffer from inadequate histopathology representation learning and inefficient molecular-pathology knowledge alignment. These two issues hinder existing methods to precisely interpret diagnostic molecular-pathology features, thereby limiting their grading performance. Moreover, the real-world applicability of existing multimodal approaches is significantly restricted as molecular biomarkers are not always available during clinical deployment. To address these problems, we introduce a novel Focus on Focus (FoF) framework with paired pathology-genomic training and applicable pathology-only inference, enhancing molecular-pathology representation effectively. Specifically, we propose a Focus-oriented Representation Learning (FRL) module to encourage the model to identify regions positively or negatively related to glioma grading and guide it to focus on the diagnostic areas with a consistency constraint. To effectively link the molecular biomarkers to morphological features, we propose a Multi-view Cross-modal Alignment (MCA) module that projects histopathology representations into molecular subspaces, aligning morphological features with corresponding molecular biomarker status by supervised contrastive learning. Experiments on the TCGA GBM-LGG dataset demonstrate that our FoF framework significantly improves the glioma grading. Remarkably, our FoF achieves superior performance using only histopathology slides compared to existing multimodal methods. The source code is available at https://github.com/peterlipan/FoF.