update

README.md

@@ -184,11 +184,21 @@ The dataset has been modified and organized for benchmarking purposes:
 
 We provide example implementations using four state-of-the-art foundation models:
 - [CONCH](https://huggingface.co/MahmoodLab/CONCH)
+- [CONCHv1.5](https://huggingface.co/MahmoodLab/conchv1_5)
 - [GigaPath](https://huggingface.co/prov-gigapath/prov-gigapath)
 - [UNI](https://huggingface.co/MahmoodLab/UNI)
 - [UNI2](https://huggingface.co/MahmoodLab/UNI2-h)
-- [H-Optimus](https://huggingface.co/bioptimus/H-optimus-0)
+- [H-Optimus-0](https://huggingface.co/bioptimus/H-optimus-0)
+- [H-Optimus-1](https://huggingface.co/bioptimus/H-optimus-1)
+- [Virchow](https://huggingface.co/paige-ai/Virchow)
 - [Virchow2](https://huggingface.co/paige-ai/Virchow2)
+- [Phikon](https://huggingface.co/owkin/phikon)
+- [Phikon-v2](https://huggingface.co/owkin/phikon-v2)
+- [Kaiko](https://github.com/kaiko-ai/towards_large_pathology_fms)
+- [Lunit](https://huggingface.co/1aurent/vit_small_patch8_224.lunit_dino)
+- [Hibou](https://huggingface.co/histai/hibou-L)
+- [CTransPath](https://github.com/Xiyue-Wang/TransPath)
+- ResNet
 
 See `licenses/references.txt` for model citations.
 
@@ -196,27 +206,51 @@ See `licenses/references.txt` for model citations.
 **Note:** The provided script is a simplified example of training code. In practice, hyperparameter tuning and additional techniques were employed to achieve the following results.
 #### Internal Split Results
 
-| Model | Accuracy | Balanced Accuracy |
-|-------|----------|------------------|
-| UNI2 | 0.8498 | 0.8500 |
-| H-Optimus | 0.8498 | 0.8398 |
-| Virchow2 | 0.8456 | 0.8355 |
-| UNI | 0.8142 | 0.7923 |
-| GigaPath | 0.8162 | 0.7877 |
-| CONCH | 0.7670 | 0.7301 |
-
+| Model | Accuracy (LogReg) | Balanced Accuracy (LogReg) | Accuracy (KNN) | Balanced Accuracy (KNN) | Accuracy (Prototype) | Balanced Accuracy (Prototype) |
+|-----|-----------------|--------------------------|--------------|-----------------------|--------------------|-----------------------------|
+| Kaiko(l14)* | 0.8608 | **0.8662** | 0.8116 | 0.7636 | 0.7708 | 0.7434 |
+| H-Optimus-1 | **0.8616** | 0.8557 | **0.8164** | **0.7671** | **0.7730** | **0.7579** |
+| UNI2 | 0.8564 | 0.8501 | 0.7962 | 0.7434 | 0.7546 | 0.7476 |
+| H-Optimus-0 | 0.8498 | 0.8399 | 0.7930 | 0.7307 | 0.7492 | 0.7321 |
+| Virchow2 | 0.8455 | 0.8351 | 0.7686 | 0.6989 | 0.6671 | 0.6500 |
+| Phikon-v2 | 0.8289 | 0.8212 | 0.7467 | 0.6777 | 0.6982 | 0.6869 |
+| Phikon | 0.8342 | 0.8111 | 0.7207 | 0.6255 | 0.6625 | 0.6385 |
+| Virchow | 0.8223 | 0.8008 | 0.7244 | 0.6262 | 0.6087 | 0.5759 |
+| Hibou | 0.8189 | 0.7985 | 0.7433 | 0.6618 | 0.6291 | 0.6034 |
+| UNI | 0.8144 | 0.7923 | 0.7634 | 0.6897 | 0.7109 | 0.6946 |
+| GigaPath | 0.8161 | 0.7878 | 0.7444 | 0.6676 | 0.6967 | 0.6675 |
+| Lunit* | 0.7919 | 0.7535 | 0.7427 | 0.6539 | 0.6611 | 0.6427 |
+| CONCHv1.5 | 0.7709 | 0.7306 | 0.7162 | 0.6313 | 0.6614 | 0.6383 |
+| CONCH | 0.7672 | 0.7295 | 0.7028 | 0.6139 | 0.6150 | 0.6097 |
+| CTransPath | 0.7255 | 0.6748 | 0.6200 | 0.5057 | 0.5158 | 0.4857 |
+| ResNet | 0.6395 | 0.5581 | 0.5114 | 0.3816 | 0.3154 | 0.2973 |
+
+
+\* Training data contains TCGA dataset.
 
 #### External Split Results
 
-| Model | Accuracy | Balanced Accuracy |
-|-------|----------|------------------|
-| UNI2 | 0.7648 | 0.7262 |
-| H-Optimus | 0.7845 | 0.7213 |
-| Virchow2 | 0.7745 | 0.6922 |
-| UNI | 0.7373 | 0.6581 |
-| GigaPath | 0.7246 | 0.6377 |
-| CONCH | 0.6991 | 0.5974 |
-
+| Model | Accuracy (LogReg) | Balanced Accuracy (LogReg) | Accuracy (KNN) | Balanced Accuracy (KNN) | Accuracy (Prototype) | Balanced Accuracy (Prototype) |
+|-----|-----------------|--------------------------|--------------|-----------------------|--------------------|-----------------------------|
+| H-Optimus-1 | **0.8080** | **0.7450** | **0.7700** | **0.6955** | **0.7572** | **0.7363** |
+| Kaiko(b8)* | 0.7920 | 0.7370 | 0.7181 | 0.6597 | 0.7509 | 0.7134 |
+| UNI2 | 0.7648 | 0.7262 | 0.7210 | 0.6498 | 0.7018 | 0.6839 |
+| H-Optimus-0 | 0.7845 | 0.7213 | 0.7209 | 0.6579 | 0.7106 | 0.6842 |
+| Virchow2 | 0.7744 | 0.6919 | 0.7221 | 0.6544 | 0.6482 | 0.6331 |
+| UNI | 0.7373 | 0.6581 | 0.6668 | 0.5887 | 0.6612 | 0.6232 |
+| Phikon-v2 | 0.7185 | 0.6535 | 0.5857 | 0.5040 | 0.6197 | 0.5752 |
+| Virchow | 0.7274 | 0.6490 | 0.6464 | 0.5541 | 0.5847 | 0.5636 |
+| GigaPath | 0.7246 | 0.6379 | 0.6426 | 0.5495 | 0.6361 | 0.5960 |
+| Phikon | 0.7311 | 0.6351 | 0.5511 | 0.4586 | 0.5474 | 0.5104 |
+| Hibou | 0.6696 | 0.6161 | 0.5155 | 0.4436 | 0.4911 | 0.4765 |
+| CONCHv1.5 | 0.7080 | 0.6098 | 0.6762 | 0.5846 | 0.6415 | 0.6100 |
+| Lunit* | 0.6851 | 0.6044 | 0.6021 | 0.5098 | 0.5862 | 0.5503 |
+| CONCH | 0.6991 | 0.5975 | 0.6626 | 0.5735 | 0.5954 | 0.5905 |
+| CTransPath | 0.6160 | 0.5215 | 0.5229 | 0.4205 | 0.4498 | 0.4128 |
+| ResNet | 0.4967 | 0.3929 | 0.3960 | 0.2871 | 0.2657 | 0.2392 |
+
+
+\* Training data contains TCGA dataset.
 
 ### Getting Started
 
@@ -292,18 +326,21 @@ dataset = wds.WebDataset(patterns[mode], shardshuffle=False) \
 model_name: "h_optimus"    # Model selection: "h_optimus", etc.
 split_type: "internal"     # Split type: "internal" or "external"
 device: "cuda"            # Computation device: "cuda" or "cpu"
+eval_name: "logreg"       # Evaluation method: "logreg", "knn", or "proto"
 feature_exist: True       # Skip feature extraction if features already exist
 max_iter: 1000           # Maximum iterations for training
-cost: 0.0001             # Cost parameter for linear classifier
+cost: 0.0001             # Cost parameter for logistic regression
 ```
 
 Configuration parameters:
 - `model_name`: Foundation model to use for feature extraction
 - `split_type`: Dataset split strategy
+- `eval_name`: Methods of evaluation (logreg, knn, proto)
 - `device`: Computation device (GPU/CPU)
 - `feature_exist`: Skip feature extraction if True and features are already available
-- `max_iter`: Maximum training iterations for the linear classifier
-- `cost`: Regularization parameter for the linear classifier
+- `max_iter`: Maximum training iterations for logistic regression
+- `cost`: Regularization parameter for logistic regression
+- `k`: Number of Nearest Neighbors in KNN
 
 2. Define models and transforms in `extract_train.py`:
 ```python
@@ -320,7 +357,7 @@ python extract_train.py
 This will:
 - Extract features using the specified foundation model
 - Save features to H5 files
-- Perform linear probing
+- Perform linear probing, KNN, and prototype classification
 - Output accuracy and balanced accuracy metrics
 
 ## License

extract_train.py

@@ -34,6 +34,7 @@ import h5py
 import numpy as np
 from tqdm import tqdm
 from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
 from sklearn.metrics import accuracy_score, balanced_accuracy_score
 from huggingface_hub import login
 import braceexpand
@@ -53,8 +54,10 @@ model_dic = {
     # if you want to use other model, please check the path
 }
 configs["model_path"] = model_dic[configs["model_name"]]
+configs["eval_name"] = configs.get("eval_name", "logreg")  # ["logreg", "knn", "proto"]
 configs["max_iter"] = configs.get("max_iter", 1000)
 configs["cost"] = configs.get("cost", 0.0001)
+configs["k"] = configs.get("k", 10)
 
 # load meta data
 metadata_path = os.path.join(work_dir, "train_val_test_split.csv")
@@ -264,11 +267,28 @@ def get_feats_labels(hdf5_file_path, mode="train", batch_size=32):
 def train_eval(train_feats, train_labels, test_feats, test_labels):
     global configs
     
-    # define model
-    model = LogisticRegression(C=configs["cost"], max_iter=configs["max_iter"])
-    model.fit(train_feats, train_labels)
-
-    pred = model.predict(test_feats)
+    # define model, train, evaluation
+    if configs["eval_name"] == "logreg":
+        model = LogisticRegression(C=configs["cost"], max_iter=configs["max_iter"])
+        model.fit(train_feats, train_labels)
+        pred = model.predict(test_feats)
+        
+    if configs["eval_name"] == "knn":
+        model = KNeighborsClassifier(n_neighbors=configs["k"])
+        model.fit(train_feats.numpy(), train_labels.numpy())
+        pred = model.predict(test_feats.numpy())
+        test_labels = test_labels.numpy()
+        
+    if configs["eval_name"] == "proto":
+        unique_labels = sorted(np.unique(train_labels.numpy()))
+        feats_proto = torch.vstack([
+            train_feats[train_labels == c].mean(dim=0) for c in unique_labels
+        ])
+        labels_proto = torch.tensor(unique_labels)
+        pw_dist = (test_feats[:, None] - feats_proto[None, :]).norm(dim=-1, p=2)
+        pred = labels_proto[pw_dist.argmin(dim=1)]     
+
+    # result
     acc = accuracy_score(test_labels, pred)
     balanced_acc = balanced_accuracy_score(test_labels, pred)
     print(f"Accuracy = {acc:.3f}, Balanced Accuracy = {balanced_acc:.3f}")

setup.ipynb

@@ -31,8 +31,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
-      "id": "FccnVVy0GAVR",
+      "source": [
+        "# check python version (this example is google colab environment)\n",
+        "!python --version"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -40,33 +42,31 @@
         "id": "FccnVVy0GAVR",
         "outputId": "e90aefeb-6cd3-4875-cc53-e7241c84589a"
       },
+      "id": "FccnVVy0GAVR",
+      "execution_count": 1,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Python 3.11.11\n"
           ]
         }
-      ],
-      "source": [
-        "# check python version (this example is google colab environment)\n",
-        "!python --version"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
-      "id": "48KZGOUK74nm",
-      "metadata": {
-        "id": "48KZGOUK74nm"
-      },
-      "outputs": [],
       "source": [
         "# set your huggingface token\n",
         "import os\n",
         "token = \"your huggingface token\""
-      ]
+      ],
+      "metadata": {
+        "id": "48KZGOUK74nm"
+      },
+      "id": "48KZGOUK74nm",
+      "execution_count": 2,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -81,8 +81,8 @@
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Cloning into 'demo'...\n",
             "remote: Enumerating objects: 199, done.\u001b[K\n",
@@ -96,7 +96,8 @@
         }
       ],
       "source": [
-        "!git clone https://oauth2:{token}@huggingface.co/datasets/dakomura/tcga-ut/"
+        "# repository url need to be changed\n",
+        "!git clone https://oauth2:{token}@huggingface.co/datasets/kooo-sh/demo/"
       ]
     },
     {
@@ -113,8 +114,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
-      "id": "RZVfsHws-djt",
+      "source": [
+        "# move directory\n",
+        "%cd repository_directory"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -122,24 +125,23 @@
         "id": "RZVfsHws-djt",
         "outputId": "83b081d6-9249-4b24-e451-c0347e752d03"
       },
+      "id": "RZVfsHws-djt",
+      "execution_count": 4,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "/content/drive/MyDrive/demo\n"
           ]
         }
-      ],
-      "source": [
-        "# move directory\n",
-        "%cd repository_directory"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
-      "id": "umN5ZMRHHA7z",
+      "source": [
+        "%ls"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -147,18 +149,17 @@
         "id": "umN5ZMRHHA7z",
         "outputId": "199d9667-1044-4c1a-e004-bf4f5b14a56d"
       },
+      "id": "umN5ZMRHHA7z",
+      "execution_count": 5,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "config.yaml  extract_train.py  \u001b[0m\u001b[01;34mlicenses\u001b[0m/  requirements.txt  train_val_test_split.csv\n",
             "\u001b[01;34mdata\u001b[0m/        \u001b[01;34mfeatures\u001b[0m/         README.md  setup.ipynb\n"
           ]
         }
-      ],
-      "source": [
-        "%ls"
       ]
     },
     {
@@ -166,16 +167,16 @@
       "execution_count": 6,
       "id": "56b69da0",
       "metadata": {
+        "id": "56b69da0",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "56b69da0",
         "outputId": "09cdfc42-751e-432c-c978-7ca0ef4379c4"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Collecting braceexpand==0.1.7 (from -r requirements.txt (line 1))\n",
             "  Downloading braceexpand-0.1.7-py2.py3-none-any.whl.metadata (3.0 kB)\n",
@@ -281,8 +282,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
-      "id": "kvflBfrrSyU4",
+      "source": [
+        "# if scikit-leran is not installed, run this command\n",
+        "# !pip install scikit-learn\n",
+        "# !pip install scipy six==1.16.0"
+      ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -291,10 +295,12 @@
         "id": "kvflBfrrSyU4",
         "outputId": "5b10426c-c3e3-4eff-cbcd-0fa1f6f96d46"
       },
+      "id": "kvflBfrrSyU4",
+      "execution_count": 7,
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
             "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.4)\n",
@@ -315,24 +321,19 @@
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
             "application/vnd.colab-display-data+json": {
-              "id": "9f6ede4ac0cd447db545799914707e3b",
               "pip_warning": {
                 "packages": [
                   "six"
                 ]
-              }
+              },
+              "id": "9f6ede4ac0cd447db545799914707e3b"
             }
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         }
-      ],
-      "source": [
-        "# if scikit-leran is not installed, run this command\n",
-        "# !pip install scikit-learn\n",
-        "# !pip install scipy six==1.16.0"
       ]
     },
     {
@@ -352,16 +353,16 @@
       "execution_count": 1,
       "id": "d3d70745",
       "metadata": {
+        "id": "d3d70745",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "d3d70745",
         "outputId": "2c674c7b-d821-43be-aad1-8172985e9439"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Collecting spams-bin\n",
             "  Downloading spams_bin-2.6.10-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (754 bytes)\n",
@@ -656,16 +657,16 @@
       "execution_count": 2,
       "id": "5f3a7c3e",
       "metadata": {
+        "id": "5f3a7c3e",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "5f3a7c3e",
         "outputId": "b7639582-b37b-4e71-e459-8b56266c7ef4"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "{'model_name': 'h_optimus', 'split_type': 'internal', 'device': 'cuda', 'feature_exist': True, 'max_iter': 1000, 'cost': 0.0001, 'model_path': 'hf-hub:bioptimus/H-optimus-0'}\n",
             "Directory already exists: ./features\n",
@@ -691,11 +692,6 @@
     }
   ],
   "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
@@ -703,8 +699,13 @@
     "language_info": {
       "name": "python",
       "version": "3.x"
-    }
+    },
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU"
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}