update README.md

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@@ -1,3 +1,4 @@
 *.svs filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
 *.ckpt filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -12,15 +12,22 @@ tags:
 
 ## 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.
+**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`**  
@@ -38,14 +45,12 @@ The pipeline still unfolds in two stages:
 ## Quick Start
  
 ### Requirements
-- NVIDIA GPU (≥ 40 GB)
-- CUDA 12.8
-- pytorch 2.7.0+cu128
+- NVIDIA GPU is required
+- Minimum 40GB GPU memory recommended
+- Tested on Ubuntu 22.04 with NVIDIA driver version 550.144.03
  
 ### Installation
 ```bash
-git clone https://huggingface.co/LGAI-EXAONE/{MODEL_NAME}.git
-cd {MODEL_NAME}
 pip install -r requirements.txt
 ```
  
@@ -55,34 +60,34 @@ from models.exaonepath import EXAONEPathV1p5Downstream
  
 hf_token = "YOUR_HUGGING_FACE_ACCESS_TOKEN"
 model = EXAONEPathV1p5Downstream.from_pretrained(
-    "LGAI-EXAONE/{MODEL_NAME}",
+    "LGAI-EXAONE/EXAONE-Path-MSI",
     use_auth_token=hf_token
 )
-probs = model("./samples/wsis/1/1.svs")
-print(f"P(CRCMSI mutant) = {probs[1]:.3f}")
+probs = model("./samples/MSI_high.svs")
+print(f"P(CRCMSI) = {probs[1]:.3f}")
 ```
  
 #### Command‑line
 ```bash
-python inference.py --svs_path ./samples/wsis/1/1.svs
+python inference.py --svs_path ./samples/MSI_high.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 |
+| 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 |  |
-| 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> |-->
+| **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> |