# Prompting Whole Slide Image Based Genetic Biomarker Prediction

arXiv: https://arxiv.org/abs/2407.09540 · HTML: https://mlsanity.com/paper/2407.09540/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images](https://mlsanity.com/paper/2303.06873/) (2023)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2307.15254/) (2023)
- [Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2303.08446/) (2023)
- [The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification](https://mlsanity.com/paper/2305.17891/) (2023)

## Built on by (most cited first, 6 of 6)

- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.18378/) (2025)
- [CLIP-IT: CLIP-based Pairing for Histology Images Classification](https://mlsanity.com/paper/2504.16181/) (2025)
- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)
- [Fourier Transform Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2510.15138/) (2025)
- [Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction from WSIs](https://mlsanity.com/paper/2603.26809/) (2026)

## Abstract (arXiv)

Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this paper, we propose a whole slide image (WSI) based genetic biomarker prediction method via prompting techniques. Our work aims at addressing the following challenges: (1) extracting foreground instances related to genetic biomarkers from gigapixel WSIs, and (2) the interaction among the fine-grained pathological components in WSIs.Specifically, we leverage large language models to generate medical prompts that serve as prior knowledge in extracting instances associated with genetic biomarkers. We adopt a coarse-to-fine approach to mine biomarker information within the tumor microenvironment. This involves extracting instances related to genetic biomarkers using coarse medical prior knowledge, grouping pathology instances into fine-grained pathological components and mining their interactions. Experimental results on two colorectal cancer datasets show the superiority of our method, achieving 91.49% in AUC for MSI classification. The analysis further shows the clinical interpretability of our method. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/PromptBio.