{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":13451,"databundleVersionId":1188070,"sourceType":"competition"}],"dockerImageVersionId":31192,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ==========================================================\n# DGPMIL – Full Pipeline with Embeddings + Graph\n# ==========================================================\nimport os, time, random\nimport numpy as np, pandas as pd\nfrom tqdm import tqdm\nimport pydicom, cv2\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, models\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# ---------------- CONFIG ----------------\nSEED = 42\nrandom.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)\nDEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Device:\", DEVICE)\n\nBASE = \"/kaggle/input/rsna-intracranial-hemorrhage-detection/rsna-intracranial-hemorrhage-detection\"\nCSV_PATH = os.path.join(BASE, \"stage_2_train.csv\")\nTRAIN_DIR = os.path.join(BASE, \"stage_2_train\")\n\nEMB_PATH = \"/kaggle/working/embeddings\"\nos.makedirs(EMB_PATH, exist_ok=True)\n\nSAMPLES_PER_CLASS = 1200\nIMG_SIZE = 256\nBATCH = 32\nEMB_BATCH = 32\nEPOCHS = 20\nLR = 3e-4\nWEIGHT_DECAY = 1e-5\nK_NEIGH = 15\nLABEL_SMOOTH = 0.05\n\n# ---------------- Helper Functions ----------------\ndef window_image(img, wl, ww):\n    minv = wl - ww/2.0\n    maxv = wl + ww/2.0\n    out = (img - minv) / (maxv - minv + 1e-9)\n    return np.clip(out, 0.0, 1.0)\n\ndef make_3ch_from_dcm(path):\n    dcm = pydicom.dcmread(path)\n    img = dcm.pixel_array.astype(np.float32)\n    ch1 = window_image(img, 40, 80)\n    ch2 = window_image(img, 80, 200)\n    ch3 = window_image(img, 600, 2800)\n    ch1 = cv2.resize(ch1,(IMG_SIZE,IMG_SIZE))\n    ch2 = cv2.resize(ch2,(IMG_SIZE,IMG_SIZE))\n    ch3 = cv2.resize(ch3,(IMG_SIZE,IMG_SIZE))\n    return np.stack([ch1,ch2,ch3],axis=0)\n\n# ---------------- Load & Balance Labels ----------------\ndf = pd.read_csv(CSV_PATH)\ndf[\"Image\"] = df[\"ID\"].apply(lambda x: x.split(\"_\")[1])\ndf[\"Subtype\"] = df[\"ID\"].apply(lambda x: x.split(\"_\")[2])\n\ndf_group = df.groupby([\"Image\",\"Subtype\"], as_index=False)[\"Label\"].max()\ndf_pivot = df_group.pivot(index=\"Image\", columns=\"Subtype\", values=\"Label\").reset_index().fillna(0)\ndf_pivot[\"Label_binary\"] = df_pivot.iloc[:,1:].max(axis=1).astype(int)\n\n# Balance classes\nsamples = []\nfor lbl in [0,1]:\n    sub = df_pivot[df_pivot[\"Label_binary\"]==lbl]\n    n = min(len(sub), SAMPLES_PER_CLASS)\n    samples.append(sub.sample(n, random_state=SEED))\ndf_bal = pd.concat(samples).reset_index(drop=True)\n\nsubtype_cols = [c for c in df_bal.columns if c not in [\"Image\",\"Label_binary\"]]\nC = len(subtype_cols)\ndf_indexed = df_bal.set_index(\"Image\")\nB_full = df_bal[subtype_cols].values.astype(float)\n\n# ---------------- Dataset ----------------\nclass RSDataset(Dataset):\n    def __init__(self, df, img_dir, img_size=IMG_SIZE, augment=False):\n        self.df = df.reset_index(drop=True)\n        self.img_dir = img_dir\n        self.img_size = img_size\n        self.augment = augment\n        self.aug_tf = transforms.Compose([\n            transforms.ToPILImage(),\n            transforms.RandomResizedCrop(img_size, scale=(0.85,1.0)),\n            transforms.RandomHorizontalFlip(),\n            transforms.RandomRotation(10),\n            transforms.ToTensor(),\n            transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])\n        ])\n        self.eval_tf = transforms.Compose([\n            transforms.ToPILImage(),\n            transforms.Resize((img_size,img_size)),\n            transforms.ToTensor(),\n            transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])\n        ])\n    def __len__(self):\n        return len(self.df)\n    def __getitem__(self, idx):\n        img_id = self.df.loc[idx,\"Image\"]\n        label = int(self.df.loc[idx,\"Label_binary\"])\n        path = os.path.join(self.img_dir,f\"ID_{img_id}.dcm\")\n        img3 = make_3ch_from_dcm(path)\n        img3 = (img3*255).astype(np.uint8)\n        tf = self.aug_tf if self.augment else self.eval_tf\n        img_t = tf(np.transpose(img3,(1,2,0)))\n        return img_t, label, img_id\n\n# ---------------- EfficientNet Embeddings ----------------\ndef make_effnet(num_classes=2):\n    eff = models.efficientnet_b0(pretrained=True)\n    in_feat = eff.classifier[1].in_features\n    eff.classifier[1] = nn.Linear(in_feat,num_classes)\n    return eff\n\neffnet = make_effnet().to(DEVICE)\nfor _, p in effnet.named_parameters(): p.requires_grad = True\n\nclass EffEmbedder(nn.Module):\n    def __init__(self, eff):\n        super().__init__()\n        self.features = eff.features\n        self.avgpool = eff.avgpool\n        self.dropout = eff.classifier[0]\n        self.feat_dim = eff.classifier[1].in_features\n    def forward(self,x):\n        x = self.features(x)\n        x = self.avgpool(x)\n        x = torch.flatten(x,1)\n        x = self.dropout(x)\n        return x\n\nembedder = EffEmbedder(effnet).to(DEVICE)\nembedder.eval()\n\ndef extract_embeddings(df_subset, batch_size=EMB_BATCH):\n    ds = RSDataset(df_subset, TRAIN_DIR, augment=False)\n    dl = DataLoader(ds, batch_size=batch_size, shuffle=False)\n    embs, ids = [], []\n    with torch.no_grad():\n        for imgs, _, id_batch in tqdm(dl):\n            imgs = imgs.to(DEVICE)\n            feat = embedder(imgs)\n            embs.append(feat.cpu().numpy())\n            ids.extend(id_batch)\n    X = np.vstack(embs)\n    # Save embeddings\n    for i, img_id in enumerate(ids):\n        np.save(os.path.join(EMB_PATH,f\"{img_id}.npy\"), X[i])\n    return X, ids\n\n# ---------------- Precompute Embeddings ----------------\ntrain_df, temp_df = train_test_split(df_bal, test_size=0.3, stratify=df_bal[\"Label_binary\"], random_state=SEED)\nval_df, test_df  = train_test_split(temp_df, test_size=0.5, stratify=temp_df[\"Label_binary\"], random_state=SEED)\n\nX_tr, ids_tr = extract_embeddings(train_df)\nX_val, ids_val = extract_embeddings(val_df)\nX_te, ids_te = extract_embeddings(test_df)\n\n# ---------------- Graph Construction ----------------\ndef construct_graph(X_images, ids_images, k_neighbors=K_NEIGH):\n    N = X_images.shape[0]\n    sim = cosine_similarity(X_images)\n    np.fill_diagonal(sim,0)\n    A_ii = np.zeros_like(sim)\n    for i in range(N):\n        nbrs = np.argsort(sim[i])[-k_neighbors:]\n        A_ii[i,nbrs] = sim[i,nbrs]\n    A_ii = np.minimum(A_ii,A_ii.T)\n    B_img = df_indexed.loc[ids_images,subtype_cols].values.astype(float)\n    row_sum = B_img.sum(axis=1,keepdims=True)\n    B_norm = B_img/(row_sum+1e-6); B_norm[row_sum.squeeze()==0]=0.0\n    A_ic = B_norm; A_ci = A_ic.T.copy()\n    p = B_full.mean(axis=0)+1e-9; co = B_full.T @ B_full\n    pmi = np.log((co+1e-6)/(p[:,None]*p[None,:])); pmi[pmi<0]=0\n    A_cc = pmi\n    top = np.concatenate([A_ii,A_ic],axis=1)\n    bottom = np.concatenate([A_ci,A_cc],axis=1)\n    A = np.concatenate([top,bottom],axis=0)\n    A += np.eye(A.shape[0])*1e-6\n    deg = A.sum(axis=1)\n    D_inv = np.diag(1/np.sqrt(deg+1e-9))\n    A_norm = D_inv @ A @ D_inv\n    X_all = np.vstack([X_images, np.zeros((C,X_images.shape[1]))])\n    X_all_t = torch.tensor(X_all,dtype=torch.float32,device=DEVICE)\n    A_norm_t = torch.tensor(A_norm,dtype=torch.float32,device=DEVICE)\n    img_idx = np.arange(N)\n    concept_idx = np.arange(N,N+C)\n    labels_img = np.array([df_indexed.loc[iid,\"Label_binary\"] for iid in ids_images],dtype=int)\n    return X_all_t,A_norm_t,img_idx,concept_idx,labels_img\n\nX_all_tr,A_tr,img_idx_tr,concept_idx_tr,labels_tr = construct_graph(X_tr,ids_tr)\nX_all_val,A_val,img_idx_val,concept_idx_val,labels_val = construct_graph(X_val,ids_val)\nX_all_te,A_te,img_idx_te,concept_idx_te,labels_te = construct_graph(X_te,ids_te)\n\n# ---------------- DGPMIL Model ----------------\nclass DGPMIL(nn.Module):\n    def __init__(self, in_feats, hidden=256, out_feats=2, num_concepts=C):\n        super().__init__()\n        self.num_concepts = num_concepts\n        self.fc1 = nn.Linear(in_feats, hidden)\n        self.fc2 = nn.Linear(hidden, hidden)\n        self.dropout = nn.Dropout(0.4)\n        self.classifier = nn.Linear(hidden, out_feats)\n    def encode(self,X_all,A_norm,concept_idx):\n        X = F.relu(self.fc1(X_all))\n        X = A_norm @ X + X\n        X = F.relu(self.fc2(X))\n        X = self.dropout(X)\n        return X\n    def forward(self,X_all,A_norm,concept_idx,img_idx):\n        H_all = self.encode(X_all,A_norm,concept_idx)\n        H_img = H_all[img_idx]\n        logits = self.classifier(H_img)\n        return logits,H_img,H_all\n\n# ---------------- Train DGPMIL ----------------\nmodel = DGPMIL(X_all_tr.shape[1]).to(DEVICE)\nopt = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\ncrit = nn.CrossEntropyLoss()\n\ndef eval_model(model,X_all,A_norm,img_idx,concept_idx,labels_img):\n    model.eval()\n    with torch.no_grad():\n        logits,_,_ = model(X_all,A_norm,concept_idx,img_idx)\n        preds = torch.argmax(logits,1).cpu().numpy()\n        probs = F.softmax(logits,dim=1)[:,1].cpu().numpy()\n        labels = labels_img\n        acc = accuracy_score(labels,preds)\n        prec = precision_score(labels,preds,zero_division=0)\n        rec = recall_score(labels,preds,zero_division=0)\n        f1 = f1_score(labels,preds,zero_division=0)\n        try: auc = roc_auc_score(labels,probs)\n        except: auc = float(\"nan\")\n    return acc,prec,rec,f1,auc\n\nbest_val_acc = -1\nfor ep in range(1,EPOCHS+1):\n    model.train()\n    opt.zero_grad()\n    labels_tr_t = torch.tensor(labels_tr,dtype=torch.long,device=DEVICE)\n    logits,_,_ = model(X_all_tr,A_tr,concept_idx_tr,img_idx_tr)\n    loss = crit(logits,labels_tr_t)\n    loss.backward()\n    opt.step()\n\n    tr_acc,tr_prec,tr_rec,tr_f1,tr_auc = eval_model(model,X_all_tr,A_tr,img_idx_tr,concept_idx_tr,labels_tr)\n    val_acc,val_prec,val_rec,val_f1,val_auc = eval_model(model,X_all_val,A_val,img_idx_val,concept_idx_val,labels_val)\n    print(f\"Ep {ep}/{EPOCHS} | Loss:{loss.item():.4f} | TrAcc:{tr_acc:.4f} ValAcc:{val_acc:.4f} | ValF1:{val_f1:.4f} ValAUC:{val_auc:.4f}\")\n\nte_acc,te_prec,te_rec,te_f1,te_auc = eval_model(model,X_all_te,A_te,img_idx_te,concept_idx_te,labels_te)\nprint(f\"\\nTest Acc:{te_acc:.4f} | F1:{te_f1:.4f} | AUC:{te_auc:.4f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-11T16:48:27.62628Z","iopub.execute_input":"2025-12-11T16:48:27.626651Z","iopub.status.idle":"2025-12-11T16:50:32.432532Z","shell.execute_reply.started":"2025-12-11T16:48:27.626625Z","shell.execute_reply":"2025-12-11T16:50:32.431556Z"}},"outputs":[],"execution_count":null}]}