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Model: ChiPhan1110/ScaleReasoner-R1 Source: Original Platform
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---
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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language:
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- en
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license: cc-by-nc-nd-4.0
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library_name: transformers
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pipeline_tag: image-text-to-text
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tags:
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- Pathology
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- VLM
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- Reasoning
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---
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<h1 align="center">[MICCAI 2026] Enhancing Pathological VLMs with Cross-scale Reasoning</h1>
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<p align="center"> Chi Phan*, Tianyi Zhang*, Qiaochu Xue, Yufeng Wu, Dan Hu, Zeyu Liu, Sudong Wang, Yueming Jin </p>
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<p align="center">
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<a href="https://conferences.miccai.org/2026/en/default.asp">
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<img src="https://img.shields.io/badge/MICCAI-2026-blue" alt="MICCAI 2026" />
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</a>
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<a href="https://arxiv.org/abs/2606.17412">
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<img src="https://img.shields.io/badge/Paper-arXiv-red" alt="Paper arXiv" />
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</a>
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<a href="https://github.com/iMVR-PL/ScaleReasoner-R1">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20GitHub-ScaleReasoner--R1-green" alt="GitHub ScaleReasoner-R1" />
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</a>
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<a href="https://huggingface.co/ChiPhan1110/ScaleReasoner-R1">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Data-Scale--VQA-yellow" alt="Data Scale-VQA" />
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</a>
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</p>
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## 🔬 Overview
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Pathological diagnosis is inherently multi-scale: pathologists reason from global tissue architecture at low magnification down to cellular morphology at high magnification, integrating evidence across views before reaching a conclusion. While existing pathological datasets for vision-language models (VLMs) include various scales, they often lack explicit cross-scale reasoning objectives. This limitation prevents VLMs from capturing essential cross-scale representations and learning evidence-based reasoning.
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We introduce the **first cross-scale training and evaluation paradigm** for pathological VLMs, along with:
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- **Scale-VQA** - a high-quality benchmark of 4,685 leakage-aware multiple-choice questions grounded in multi-magnification pathology images across 15 organs and 5 clinically-aligned reasoning dimensions.
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- **ScaleReasoner-R1** - a pathology VLM trained with Group Relative Policy Optimization (GRPO) that achieves state-of-the-art performance on cross-scale multi-image VQA and transfers strongly to established single-image benchmarks.
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## 🧩 Method
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<div align="center">
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<img src="assets/method_fig.png" alt="Method Overview" width="95%">
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**(a)** Leakage-aware curation pipeline for Scale-VQA. **(b)** Dataset overview across organs and magnifications. **(c)** GRPO-based reinforcement learning framework for ScaleReasoner-R1.
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</div>
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### Scale-VQA: Leakage-Aware Cross-Scale Benchmark
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Naively constructed cross-scale VQA benchmarks suffer from **text-only shortcut solutions** — models can infer the correct answer from linguistic or biomedical priors without ever examining the images. Our curation pipeline eliminates these via three steps:
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| Step | Description |
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|------|-------------|
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| **Scale-specific Feature Decomposition** | Expert annotations are decomposed into per-scale evidence sets; initial visual-grounding and scale-dependency constraints are imposed. |
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| **Text-only Adversarial Screening** | Gemini 3 Pro and Qwen3-Max act as text-only adversaries. If either model answers correctly without images, constraints are tightened and questions are regenerated. |
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| **Cross-scale MCQ Construction & Clinical Validation** | Final MCQs are reviewed by senior pathologists to confirm that correct answers are visually grounded and distractors are clinically plausible. |
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### ScaleReasoner-R1: Cross-scale Reasoning via RL
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ScaleReasoner-R1 is initialized from **Patho-R1-7B** and fine-tuned with **GRPO** on Scale-VQA. Given a multi-scale image set I = {I_(10x), I_(40x), I_(200x)}, a question, and options, the model generates a structured response with a reasoning trace (`<think>`) followed by a final answer (`<answer>`).
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**Training Dynamics:**
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<div align="center">
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| Training Reward | Validation Accuracy |
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|:-------------------:|:---------------:|
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| <img src="assets/rl_curve_critic.png" width="420"> | <img src="assets/rl_curve_val.png" width="500"> |
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</div>
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## 🏆 Results
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<div align="center">
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<img src="assets/eval.png" alt="Evaluation Results" width="80%">
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</div>
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### Cross-scale Multi-image VQA
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| Model | Corresp. | Confirm. | Localiz. | Explan. | Diagno. | **AVG** |
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|-------|:--------:|:--------:|:--------:|:-------:|:-------:|:-------:|
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| Qwen2.5-VL-7B | 41.79 | 47.26 | 71.14 | 44.28 | 63.68 | 53.63 |
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| Gemini 3 Flash | 48.26 | 58.71 | 71.64 | 53.73 | 72.14 | 60.90 |
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| GPT-5.2 | 47.76 | 59.70 | 74.13 | 45.77 | 65.17 | 58.51 |
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| LLaVA-Med-7B | 25.87 | 16.92 | 28.36 | 20.90 | 24.88 | 23.38 |
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| Quilt-LLaVA | 32.34 | 14.43 | 45.27 | 30.35 | 29.35 | 30.35 |
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| CLOVER | 37.31 | 61.69 | 73.13 | 46.27 | 65.77 | 56.82 |
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| Patho-R1 | 31.84 | 40.30 | 59.70 | 56.22 | 68.66 | 51.34 |
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| **ScaleReasoner-R1** | **80.60** | **89.05** | **84.58** | **76.12** | **84.08** | **82.89** |
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### Single-image VQA (PathMMU)
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| Model | Overall | PubMed | SocialPath | EduContent | Atlas | PathCLS |
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|-------|:------------:|:------:|:----------:|:----------:|:-----:|:-------:|
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| Patho-R1 | 64.8 | 68.7 | 63.9 | 65.7 | 73.5 | 41.8 |
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| **ScaleReasoner-R1** | **66.2** | **71.2** | **67.6** | **67.6** | **79.3** | 37.9 |
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---
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## 📁 Repository Structure
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```text
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ScaleReasoner-R1/
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├── assets/
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├── data/ # Cross-scale VQA json by split
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├── preprocess/
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│ ├── generate_vqa_data/ # Feature extraction, VQA generation, split creation
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│ └── prompts/ # Leakage-aware prompt templates and constraints
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├── script/
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│ ├── preprocess/ # End-to-end preprocessing entrypoints
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│ ├── train/ # SFT and GRPO launch scripts
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│ └── postprocess/ # Postprocess after training
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├── LLaMA-Factory/ # SFT training framework
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└── verl/ # RL training framework
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```
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## 🚀 Getting Started
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### Environment Setup
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```bash
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# Clone the repository
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git clone https://github.com/iMVR-PL/ScaleReasoner-R1.git
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cd ScaleReasoner-R1
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# Set up environment variables
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cp script/.env.example script/.env
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# Edit script/.env with your paths and API keys
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```
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Configure `script/.env`:
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```bash
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# Data paths
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DATA_DIR=/path/to/triplet_raw_data
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ROOT=/path/to/ScaleReasoner-R1
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PROCESSED_DIR=/path/to/processed_data
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# Model paths
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ACTOR_MODEL_DIR=/path/to/patho-r1-7b # base model (Patho-R1-7B)
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RESULTS_DIR=/path/to/results
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# Logging
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LOG_DIR=/path/to/logs
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WANDB_DIR=/path/to/wandb
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# API keys (for VQA generation pipeline)
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GEMINI_API_KEY=...
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OPENAI_API_KEY=...
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DASHSCOPE_API_KEY=...
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HF_TOKEN=...
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```
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**RL environment (verl).** ScaleReasoner-R1 is trained with [verl](https://github.com/volcengine/verl). Please follow the [verl installation guide](https://verl.readthedocs.io/en/latest/start/install.html) to set up the environment, then install the local copy:
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```bash
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conda create -n verl python=3.10 -y && conda activate verl
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pip install -e verl/
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```
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**SFT environment (LLaMA-Factory).** The SFT baseline uses [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory):
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```bash
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conda create -n sft python=3.10 -y && conda activate sft
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pip install -e LLaMA-Factory/
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```
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> Both environments require CUDA 12.1+ and PyTorch 2.3+. We recommend using separate conda environments for RL and SFT to avoid dependency conflicts.
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## 🗂️ Dataset
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Download **Scale-VQA** from [HuggingFace](https://huggingface.co/datasets/iMVR-PL/Scale-VQA). The `data/` directory contains the train/val/test splits in JSON format. Each sample includes:
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```json
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{
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"question": "...",
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"options": { "A": "...", "B": "...", "C": "...", "D": "..." },
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"answer": "D",
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"rationale": "...",
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"image_path": {
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"low_mag": "wsi_id/rois/10_....jpg",
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"mid_mag": "wsi_id/rois/40_....jpg",
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"high_mag": "wsi_id/rois/200_....jpg"
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}
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}
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```
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## 🤖 Training & Inference
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### RL Training (ScaleReasoner-R1)
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We use [**verl**](https://github.com/volcengine/verl) for GRPO-based RL training:
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```bash
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conda create -n verl python=3.10 -y && conda activate verl
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pip install -e verl/
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bash script/train/run_grpo_cross_scale_vqa.sh
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```
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Key hyperparameters: `n=5` rollouts per question, `total_epochs=5`, `train_batch_size=32`. The custom reward function is at `verl/verl/utils/reward_score/cross_scale_vqa.py`.
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### SFT Baseline
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We use [**LLaMA-Factory**](https://github.com/hiyouga/LLaMA-Factory) for supervised fine-tuning:
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```bash
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conda create -n sft python=3.10 -y && conda activate sft
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pip install -e LLaMA-Factory/
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bash script/train/run_sft_pathor1_new_triplet_mcq_think_only.sh
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```
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### Inference
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Download **ScaleReasoner-R1** from [HuggingFace](https://huggingface.co/ChiPhan1110/ScaleReasoner-R1). ScaleReasoner-R1 was trained to produce structured outputs using `<think>` and `<answer>` tags. To ensure reproducible results, pass the system prompt below:
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```python
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SYSTEM_PROMPT = (
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"You are a pathology expert. Read the question and options about the image carefully. "
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"Think step by step inside <think> </think>. Then output ONLY the SINGLE best option letter "
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"inside <answer> </answer>.
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"
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"Example: <think>Your reasoning</think> <answer>A</answer>. "
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"Do not include the option text or any extra words inside <answer> </answer> tags."
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)
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```
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**Option 1 — vLLM server (recommended for batched evaluation)**
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```bash
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vllm serve <path/to/ScaleReasoner-R1> \
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--host 0.0.0.0 \
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--port 8000 \
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--tensor-parallel-size 1 \
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--max-model-len 8192 \
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--limit-mm-per-prompt.image 5
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```
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Then query via the OpenAI-compatible client:
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```python
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from openai import OpenAI
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import base64
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def encode_image(path):
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with open(path, "rb") as f:
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return base64.b64encode(f.read()).decode("utf-8")
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")
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response = client.chat.completions.create(
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model="ChiPhan1110/ScaleReasoner-R1",
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messages=[{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encode_image('low_mag.jpg')}"}},
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encode_image('mid_mag.jpg')}"}},
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encode_image('high_mag.jpg')}"}},
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{"type": "text", "text": "<question>
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(A) ...
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(B) ...
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(C) ...
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(D) ..."},
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]
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}],
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max_tokens=4096,
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)
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print(response.choices[0].message.content)
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```
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**Option 2 — Hugging Face Transformers**
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```python
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
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from qwen_vl_utils import process_vision_info
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"ChiPhan1110/ScaleReasoner-R1", torch_dtype="auto", device_map="auto"
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)
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processor = AutoProcessor.from_pretrained("ChiPhan1110/ScaleReasoner-R1")
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messages = [{
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"role": "user",
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"content": [
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{"type": "image", "image": "low_mag.jpg"},
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{"type": "image", "image": "mid_mag.jpg"},
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{"type": "image", "image": "high_mag.jpg"},
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{"type": "text", "text": "<question>
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(A) ...
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(B) ...
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(C) ...
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(D) ..."},
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]
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}]
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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image_inputs, _ = process_vision_info(messages)
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inputs = processor(text=[text], images=image_inputs, return_tensors="pt").to(model.device)
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output = model.generate(**inputs, max_new_tokens=4096)
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print(processor.decode(output[0][len(inputs.input_ids[0]):], skip_special_tokens=True))
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```
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## 🙏 Acknowledgements
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This work was supported by the Ministry of Education, Singapore, under the Tier 1 grant (24-1250-P0001) and Tier 2 grant (T2EP20224-0028), and by PuzzleLogic Pte Ltd, Singapore.
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We gratefully acknowledge the open-source projects that made the development of **ScaleReasoner-R1** possible:
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- [**verl**](https://github.com/volcengine/verl), for the reinforcement learning training framework.
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- [**LLaMA-Factory**](https://github.com/hiyouga/LLaMA-Factory), for the unified fine-tuning pipelines.
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- [**vLLM**](https://github.com/vllm-project/vllm), for efficient large language model inference and serving.
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We also acknowledge the following open-source models used for comparison in our experiments: [Qwen2.5-VL-7B](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), [LLaVA-Med-7B](https://github.com/microsoft/LLaVA-Med), [HuaTuoGPT-7B](https://github.com/FreedomIntelligence/HuatuoGPT-Vision), [Lingshu-7B](https://huggingface.co/lingshu-medical-mllm/Lingshu-7B), [Quilt-LLaVA](https://github.com/aldraus/quilt-llava), [CLOVER](https://huggingface.co/jline/CLOVER-Qwen2.5-VL), and [Patho-R1](https://github.com/wenchuan-zhang/patho-r1).
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We sincerely thank the developers and contributors of these projects for their excellent work and for making their code and models publicly available to the research community.
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## ❤️ Citation
|
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If you find our work helpful, please consider citing our paper and the frameworks we build upon:
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```bibtex
|
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@article{phan2026enhancing,
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title={Enhancing Pathological VLMs with Cross-scale Reasoning},
|
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author={Phan, Chi and Zhang, Tianyi Beetroot and Xue, Qiaochu and Wu, Yufeng and Hu, Dan and Liu, Zeyu and Wang, Sudong and Jin, Yueming},
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journal={arXiv preprint arXiv:2606.17412},
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year={2026}
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}
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```
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24
added_tokens.json
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24
added_tokens.json
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{
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"</tool_call>": 151658,
|
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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||||
"<|endoftext|>": 151643,
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chat_template.jinja
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7
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|
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{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
|
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|
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|
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config.json
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6
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||||
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|
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|
||||
37
preprocessor_config.json
Normal file
37
preprocessor_config.json
Normal file
@@ -0,0 +1,37 @@
|
||||
{
|
||||
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|
||||
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|
||||
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||||
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
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|
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|
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|
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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size 11421896
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209
tokenizer_config.json
Normal file
209
tokenizer_config.json
Normal file
@@ -0,0 +1,209 @@
|
||||
{
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|
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||||
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||||
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|
||||
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||||
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|
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
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|
||||
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|
||||
44
video_preprocessor_config.json
Normal file
44
video_preprocessor_config.json
Normal file
@@ -0,0 +1,44 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
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||||
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|
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|
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user