# Historical Report Guided Bi-modal Concurrent Learning for Pathology Report Generation

arXiv: https://arxiv.org/abs/2506.18658 · HTML: https://mlsanity.com/paper/2506.18658/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models](https://mlsanity.com/paper/2403.06764/) (2024)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation](https://mlsanity.com/paper/2010.10042/) (2020)
- [Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2303.08446/) (2023)
- [HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction](https://mlsanity.com/paper/2403.05396/) (2024)
- [WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images](https://mlsanity.com/paper/2311.16480/) (2023)
- [Beyond Text: Frozen Large Language Models in Visual Signal Comprehension](https://mlsanity.com/paper/2403.07874/) (2024)
- [Complex Organ Mask Guided Radiology Report Generation](https://mlsanity.com/paper/2311.02329/) (2023)
- [SimTriplet: Simple Triplet Representation Learning with a Single GPU](https://mlsanity.com/paper/2103.05585/) (2021)
- [Clinical-grade Multi-Organ Pathology Report Generation for Multi-scale Whole Slide Images via a Semantically Guided Medical Text Foundation Model](https://mlsanity.com/paper/2409.15574/) (2024)

## Built on by (most cited first, 7 of 7)

- [RANGER: Sparsely-Gated Mixture-of-Experts with Adaptive Retrieval Re-ranking for Pathology Report Generation](https://mlsanity.com/paper/2603.04348/) (2026)
- [Semantic Context-aware mOdality fUsion Transformer (SCOUT): A Context-Aware Multimodal Transformer for Concept-Grounded Pathology Report Generation](https://mlsanity.com/paper/2605.01144/) (2026)
- [AMRG: Extend Vision Language Models for Automatic Mammography Report Generation](https://mlsanity.com/paper/2508.09225/) (2025)
- [Prototype-Based Knowledge Guidance for Fine-Grained Structured Radiology Reporting](https://mlsanity.com/paper/2603.11938/) (2026)
- [Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation](https://mlsanity.com/paper/2607.00499/) (2026)
- [PathReportEval: A Systematic Benchmark for Pathology Report Generation](https://mlsanity.com/paper/2607.18448/) (2026)
- [Pathologist Attention-Aligned Report Generation for Prostate Histopathology](https://mlsanity.com/paper/2607.19624/) (2026)

## Abstract (arXiv)

Automated pathology report generation from Whole Slide Images (WSIs) faces two key challenges: (1) lack of semantic content in visual features and (2) inherent information redundancy in WSIs. To address these issues, we propose a novel Historical Report Guided Bi-modal Concurrent Learning Framework for Pathology Report Generation (BiGen) emulating pathologists' diagnostic reasoning, consisting of: (1) A knowledge retrieval mechanism to provide rich semantic content, which retrieves WSI-relevant knowledge from pre-built medical knowledge bank by matching high-attention patches and (2) A bi-modal concurrent learning strategy instantiated via a learnable visual token and a learnable textual token to dynamically extract key visual features and retrieved knowledge, where weight-shared layers enable cross-modal alignment between visual features and knowledge features. Our multi-modal decoder integrates both modals for comprehensive diagnostic reports generation. Experiments on the PathText (BRCA) dataset demonstrate our framework's superiority, achieving state-of-the-art performance with 7.4% relative improvement in NLP metrics and 19.1% enhancement in classification metrics for Her-2 prediction versus existing methods. Ablation studies validate the necessity of our proposed modules, highlighting our method's ability to provide WSI-relevant rich semantic content and suppress information redundancy in WSIs. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/BiGen.