| | --- |
| | 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](https://huggingface.co/LGAI-EXAONE/EXAONE-Path-1.5)** 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. |
| | |
| |
|
| | --- |
| | |
| | |
| | <p align="center"> |
| | <img src="assets/architecture.png" width="786"/> |
| | </p> |
| | |
| | |
| | ## 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 is required |
| | - Minimum 40GB GPU memory recommended |
| | - Tested on Ubuntu 22.04 with NVIDIA driver version 550.144.03 |
| | |
| | ### Installation |
| | ```bash |
| | 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/EXAONE-Path-MSI", |
| | use_auth_token=hf_token |
| | ) |
| | probs = model("./samples/MSI_high.svs") |
| | print(f"P(CRCMSI) = {probs[1]:.3f}") |
| | ``` |
| | |
| | #### Command‑line |
| | ```bash |
| | python inference.py --svs_dir ./samples |
| | ``` |
| | |
| | |
| | ### 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) | EXAONEPath V1.5 | **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 | 0.6842 | |
| | | LUAD-EGFR-mut | 0.8197 | 0.8152 | 0.7691 | 0.7623 | 0.8577 | 0.7607 | 0.8564 | |
| | | LUAD-KRAS-mut | 0.5405 | 0.6299 | 0.4676 | 0.5110 | 0.4690 | 0.5480 | 0.6038 | |
| | | BRCA-ER | 0.9343 | 0.8998 | 0.9115 | 0.9186 | 0.9454 | 0.9096 | 0.9278 | |
| | | BRCA-PR | 0.8804 | 0.8613 | 0.8470 | 0.8595 | 0.8770 | 0.8215 | 0.8430 | |
| | | BRCA-HER2 | 0.8046 | 0.8154 | 0.7822 | 0.7891 | 0.8322 | 0.7811 | 0.8050 | |
| | | BRCA-TP53 | 0.7879 | 0.8415 | 0.7879 | 0.7388 | 0.8080 | 0.6607 | 0.7656 | |
| | | BRCA-PIK3CA | 0.7577 | 0.8929 | 0.7015 | 0.7347 | 0.8571 | 0.7066 | 0.7908 | |
| | | RCC-PBRM1 | 0.6383 | 0.5570 | 0.5129 | 0.5270 | 0.5011 | 0.4445 | 0.5780 | |
| | | RCC-BAP1 | 0.7188 | 0.7690 | 0.7310 | 0.6970 | 0.7160 | 0.7337 | 0.7323 | |
| | | COAD-KRAS | 0.7642 | 0.7443 | 0.6989 | 0.8153 | 0.9432 | 0.6790 | 0.8693 | |
| | | COAD-TP53 | 0.8889 | 0.8160 | 0.7014 | 0.7118 | 0.7830 | 0.8785 | 0.8715 | |
| | | <span style="color:red">**Average**</span> | 0.7817 | 0.7869 | 0.7299 | 0.7457 | <u>0.7876</u> | 0.7356 | <span style="color:red">**0.7932**</span> | |
| |
|
| | ## License |
| | The model is licensed under [EXAONEPath AI Model License Agreement 1.0 - NC](./LICENSE) |
