---
license: other
license_name: exaonepath
license_link: LICENSE
tags:
- lg-ai
- EXAONEPath-1.5
- pathology
---
<!--# EXAONE Path for CRCMSI – CRCMSI-centric Whole-Slide Image Classifier
*A purpose-built upgrade of **EXAONE Path 1.5***-->

## Introduction
<!--**EXAONE Path for CRCMSI** is an **enhanced whole-slide image (WSI) classification framework** that retains the core architecture of EXAONE Path 1.5 while upgrading its internals for greater efficiency and richer multimodal integration.-->
**EXAONE Path MSI** is an **enhanced whole-slide image (WSI) classification framework** that retains the core architecture of EXAONE Path while upgrading its internals for greater efficiency and richer multimodal integration.
 
The pipeline still unfolds in two stages:  

1. **Patch-wise feature extraction** – Each WSI is tiled into 256 × 256 px patches, which are embedded into 768-dimensional vectors using the frozen **[EXAONE Path](https://huggingface.co/LGAI-EXAONE/EXAONEPath)** encoder.  
2. **Slide-level aggregation** – The patch embeddings are aggregated using a Vision Transformer, producing a unified slide-level representation that a lightweight classification head transforms into task-specific probabilities.  
 
---
 
## Key Improvements
 
- **[FlexAttention](https://pytorch.org/blog/flexattention/) + `torch.compile`**  
  *What changed:* Replaced vanilla multi‑head self‑attention with IO‑aware **FlexAttention** kernels and enabled `torch.compile` to fuse the forward/backward graph at runtime. The new kernel layout dramatically improves both memory efficiency and training-and-inference throughput.
 
- **Coordinate‑aware Relative Bias**  
  *What changed:* Added an ALiBi‑style distance bias that is computed from the (x, y) patch coordinates themselves, allowing the ViT aggregator to reason about spatial proximity.
 
- **Scalable Mixed‑Omics Encoder (Token‑mixing Transformer)**  
  *What changed:* Each omics modality is first tokenised into a fixed‑length set. **All modality‑specific tokens are concatenated into a single sequence and passed through a shared multi‑head self‑attention stack**, enabling direct information exchange across modalities in one shot. The aggregated omics representation is subsequently fused with image tokens via cross‑attention. This release uses **three modalities (RNA, CNV, DNA‑methylation)**, but the design is agnostic to modality count and scales linearly with token number.
 
---
 
 
## Quick Start
 
### Requirements
- NVIDIA GPU (≥ 40 GB)
- CUDA 12.8
- pytorch 2.7.0+cu128
 
### Installation
```bash
git clone https://huggingface.co/LGAI-EXAONE/{MODEL_NAME}.git
cd {MODEL_NAME}
pip install -r requirements.txt
```
 
### Quick Inference
```python
from models.exaonepath import EXAONEPathV1p5Downstream
 
hf_token = "YOUR_HUGGING_FACE_ACCESS_TOKEN"
model = EXAONEPathV1p5Downstream.from_pretrained(
    "LGAI-EXAONE/{MODEL_NAME}",
    use_auth_token=hf_token
)
probs = model("./samples/wsis/1/1.svs")
print(f"P(CRCMSI mutant) = {probs[1]:.3f}")
```
 
#### Command‑line
```bash
python inference.py --svs_path ./samples/wsis/1/1.svs
```
 
 
### Model Performance Comparison
 
| Metric (AUC) / Task                | Titan (Conch v1.5 + iBot, image-text) | PRISM (virchow + perceiver, image-text) | CHIEF (CTransPath + CLAM, image-text, WSI-contrastive) | Prov-GigaPath (GigaPath + LongNet, image-only, mask-prediction) | UNI2-h + CLAM (image-only) | EXAONE Path 1.5 | EXAONE Path MSI |
|------------------------------------|---------------------------------------|-----------------------------------------|--------------------------------------------------------|-----------------------------------------------------------------|---------------------------|------------------------|------------------------|
| **CRC-MSI**                    | 0.9370 | 0.9432 | 0.9273 | 0.9541 | <u>0.9808</u> | 0.9537 |**0.9844** |
<!--| LUAD-TMB (cutoff 10)           | 0.6901 | 0.6445 | 0.6501 | 0.6744 | 0.6686 | 0.6846 |  |
| LUAD-EGFR-mut                  | 0.8197 | 0.8152 | 0.7691 | 0.7623 | 0.8577 | 0.7607 |  |
| LUAD-KRAS-mut                  | 0.5405 | 0.6299 | 0.4676 | 0.5110 | 0.4690 | 0.5480 |  |
| BRCA-ER                        | 0.9343 | 0.8998 | 0.9115 | 0.9186 | 0.9454 | 0.9096 |  |
| BRCA-PR                        | 0.8804 | 0.8613 | 0.8470 | 0.8595 | 0.8770 | 0.8215 |  |
| BRCA-HER2                      | 0.8046 | 0.8154 | 0.7822 | 0.7891 | 0.8322 | 0.7811 |  |
| BRCA-TP53                      | 0.7879 | 0.8415 | 0.7879 | 0.7388 | 0.8080 | 0.6607 |  |
| BRCA-PIK3CA                    | 0.7577 | 0.8929 | 0.7015 | 0.7347 | 0.8571 | 0.7066 |  |
| RCC-PBRM1                      | 0.6383 | 0.5570 | 0.5129 | 0.5270 | 0.5011 | 0.4445 |  |
| RCC-BAP1                       | 0.7188 | 0.7690 | 0.7310 | 0.6970 | 0.7160 | 0.7337 |  |
| COAD-KRAS                      | 0.7642 | 0.7443 | 0.6989 | 0.8153 | 0.9432 | 0.6790 |  |
| COAD-TP53                      | 0.8889 | 0.8160 | 0.7014 | 0.7118 | 0.7830 | 0.8785 |  |
| <span style="color:red">**Average**</span> | 0.7817 | 0.7869 | 0.7299 | 0.7457 | <u>0.7876</u> |  <span style="color:red">**0.7932**</span> |-->