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{
"cells": [
{
"cell_type": "markdown",
"id": "5a6c76f2",
"metadata": {},
"source": [
"## Chest2VEC"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26215417",
"metadata": {},
"outputs": [],
"source": [
"from chest2vec import Chest2Vec\n",
"import os\n",
"os.environ[\"HF_HOME\"] = \"/model/huggingface\"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n",
"\n",
"\n",
"\n",
"m = Chest2Vec.from_pretrained(\"chest2vec/chest2vec_0.6b_cxr\", device=\"cuda:0\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "624ad061",
"metadata": {},
"outputs": [],
"source": [
"instructions = [\"Find findings about the lungs.\"]\n",
"queries = [\"Consolidation in the right lower lobe.\"]\n",
"\n",
"out = m.embed_instruction_query(instructions, queries, max_len=512, batch_size=8)\n",
"\n",
"# Global embedding (derived): mean of 9 section vectors then L2-normalized\n",
"g = out.global_embedding # [N, H]\n",
"\n",
"# Per-section embeddings (by full name)\n",
"lung = out.by_section_name[\"Lungs and Airways\"] # [N, H]\n",
"imp = out.by_section_name[\"impression\"] # [N, H]\n",
"\n",
"# Or use aliases (case-insensitive)\n",
"lung = out.by_alias[\"lungs\"] # [N, H]\n",
"cardio = out.by_alias[\"cardio\"] # [N, H]\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b083b9a8",
"metadata": {},
"outputs": [],
"source": [
"candidates = [\n",
" \"Lungs are clear. No focal consolidation.\",\n",
" \"Pleural effusion on the left.\",\n",
" \"Right lower lobe consolidation.\",\n",
" \"Cardiomediastinal silhouette is normal.\"\n",
"]\n",
"\n",
"cand_out = m.embed_texts(candidates, max_len=512, batch_size=16)\n",
"\n",
"cand_global = cand_out.global_embedding # [N, H]\n",
"cand_lung = cand_out.by_alias[\"lungs\"] # [N, H]\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "98ebf6d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top-5 scores: tensor([ 0.3646, -0.0407, -0.0810, -0.1504])\n",
"Top-5 indices: tensor([2, 1, 3, 0])\n"
]
}
],
"source": [
"# Query embeddings for \"Lungs and Airways\" section\n",
"q = out.global_embedding # [Nq, H]\n",
"\n",
"# Document embeddings for \"Lungs and Airways\" section\n",
"d = cand_out.global_embedding # [Nd, H]\n",
"\n",
"# Compute top-k cosine similarities\n",
"scores, idx = Chest2Vec.cosine_topk(q, d, k=5, device=\"cuda\")\n",
"# scores: [Nq, k] - similarity scores\n",
"# idx: [Nq, k] - indices of top-k candidates\n",
"\n",
"print(f\"Top-5 scores: {scores[0]}\")\n",
"print(f\"Top-5 indices: {idx[0]}\")\n"
]
},
{
"cell_type": "markdown",
"id": "906d89b8",
"metadata": {},
"source": [
"## CT"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "347dd738",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"EmbedOutput(section_matrix=tensor([[[ 6.9175e-02, -1.6013e-04, -1.1102e-02, ..., -3.0460e-02,\n",
" -5.8357e-02, 3.6722e-02],\n",
" [ 7.2193e-02, -6.4974e-04, -1.3356e-02, ..., -2.9022e-02,\n",
" -6.0931e-02, 3.6963e-02],\n",
" [ 7.2526e-02, -2.2293e-03, -1.8355e-02, ..., -2.7643e-02,\n",
" -6.0637e-02, 4.1613e-02],\n",
" ...,\n",
" [ 7.4716e-02, -3.1762e-03, -2.3746e-02, ..., -1.9208e-02,\n",
" -5.9592e-02, 4.8399e-02],\n",
" [ 7.0074e-02, -5.4069e-05, -1.2760e-02, ..., -2.8404e-02,\n",
" -5.8251e-02, 3.8930e-02],\n",
" [ 7.5282e-02, -3.2632e-03, -2.3526e-02, ..., -1.8972e-02,\n",
" -6.0407e-02, 4.7962e-02]]]), global_embedding=tensor([[ 0.0731, -0.0017, -0.0180, ..., -0.0245, -0.0603, 0.0423]]), by_section_name={'Lungs and Airways': tensor([[ 0.0692, -0.0002, -0.0111, ..., -0.0305, -0.0584, 0.0367]]), 'Pleura': tensor([[ 0.0722, -0.0006, -0.0134, ..., -0.0290, -0.0609, 0.0370]]), 'Cardiovascular': tensor([[ 0.0725, -0.0022, -0.0184, ..., -0.0276, -0.0606, 0.0416]]), 'Hila and Mediastinum': tensor([[ 0.0749, -0.0023, -0.0224, ..., -0.0191, -0.0601, 0.0463]]), 'Tubes & Devices': tensor([[ 0.0730, -0.0007, -0.0161, ..., -0.0234, -0.0616, 0.0395]]), 'Musculoskeletal and Chest Wall': tensor([[ 0.0740, -0.0023, -0.0202, ..., -0.0237, -0.0614, 0.0432]]), 'Abdominal': tensor([[ 0.0747, -0.0032, -0.0237, ..., -0.0192, -0.0596, 0.0484]]), 'impression': tensor([[ 7.0074e-02, -5.4069e-05, -1.2760e-02, ..., -2.8404e-02,\n",
" -5.8251e-02, 3.8930e-02]]), 'Other': tensor([[ 0.0753, -0.0033, -0.0235, ..., -0.0190, -0.0604, 0.0480]])}, by_alias={'global': tensor([[ 0.0731, -0.0017, -0.0180, ..., -0.0245, -0.0603, 0.0423]]), 'lungs': tensor([[ 0.0692, -0.0002, -0.0111, ..., -0.0305, -0.0584, 0.0367]]), 'lung': tensor([[ 0.0692, -0.0002, -0.0111, ..., -0.0305, -0.0584, 0.0367]]), 'pleura': tensor([[ 0.0722, -0.0006, -0.0134, ..., -0.0290, -0.0609, 0.0370]]), 'cardio': tensor([[ 0.0725, -0.0022, -0.0184, ..., -0.0276, -0.0606, 0.0416]]), 'cardiovascular': tensor([[ 0.0725, -0.0022, -0.0184, ..., -0.0276, -0.0606, 0.0416]]), 'hila': tensor([[ 0.0749, -0.0023, -0.0224, ..., -0.0191, -0.0601, 0.0463]]), 'mediastinum': tensor([[ 0.0749, -0.0023, -0.0224, ..., -0.0191, -0.0601, 0.0463]]), 'tubes': tensor([[ 0.0730, -0.0007, -0.0161, ..., -0.0234, -0.0616, 0.0395]]), 'devices': tensor([[ 0.0730, -0.0007, -0.0161, ..., -0.0234, -0.0616, 0.0395]]), 'msk': tensor([[ 0.0740, -0.0023, -0.0202, ..., -0.0237, -0.0614, 0.0432]]), 'musculoskeletal': tensor([[ 0.0740, -0.0023, -0.0202, ..., -0.0237, -0.0614, 0.0432]]), 'abd': tensor([[ 0.0747, -0.0032, -0.0237, ..., -0.0192, -0.0596, 0.0484]]), 'abdominal': tensor([[ 0.0747, -0.0032, -0.0237, ..., -0.0192, -0.0596, 0.0484]]), 'impression': tensor([[ 7.0074e-02, -5.4069e-05, -1.2760e-02, ..., -2.8404e-02,\n",
" -5.8251e-02, 3.8930e-02]]), 'other': tensor([[ 0.0753, -0.0033, -0.0235, ..., -0.0190, -0.0604, 0.0480]])})"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# !pip install nibabel, monai\n",
"import numpy as np\n",
"from pathlib import Path\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Optional (for nicer overlays). If scipy isn't installed, code will fall back.\n",
"try:\n",
" from scipy.ndimage import binary_erosion\n",
" _HAS_SCIPY = True\n",
"except Exception:\n",
" _HAS_SCIPY = False\n",
"\n",
"# Point this to your preprocessed folder\n",
"NPZ_ROOT = Path(\"./data/preprocessed\") # <-- EDIT\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "edbddaf7",
"metadata": {},
"outputs": [],
"source": [
"def load_npz_case(npz_path: Path):\n",
" npz_path = Path(npz_path)\n",
" with np.load(npz_path, allow_pickle=False) as z:\n",
" keys = list(z.keys())\n",
"\n",
" # Support a few common key names\n",
" if \"ct\" in keys:\n",
" ct = z[\"ct\"]\n",
" elif \"image\" in keys:\n",
" ct = z[\"image\"]\n",
" else:\n",
" raise KeyError(f\"No CT key found. Available keys: {keys}\")\n",
"\n",
" rex = z[\"rex\"] if \"rex\" in keys else None\n",
" tot = z[\"totalseg\"] if \"totalseg\" in keys else (z[\"label\"] if \"label\" in keys else None)\n",
"\n",
" # Basic sanity checks\n",
" assert ct.ndim == 4 and ct.shape[0] == 1, f\"Expected ct shape (1,D,H,W), got {ct.shape}\"\n",
" D, H, W = ct.shape[1], ct.shape[2], ct.shape[3]\n",
"\n",
" if tot is not None:\n",
" assert tot.ndim == 4 and tot.shape[0] == 1, f\"Expected totalseg shape (1,D,H,W), got {tot.shape}\"\n",
" assert tot.shape[1:] == (D, H, W), f\"totalseg spatial mismatch: {tot.shape} vs ct {ct.shape}\"\n",
"\n",
" if rex is not None:\n",
" assert rex.ndim == 4, f\"Expected rex shape (F,D,H,W), got {rex.shape}\"\n",
" assert rex.shape[1:] == (D, H, W), f\"rex spatial mismatch: {rex.shape} vs ct {ct.shape}\"\n",
"\n",
" return ct, rex, tot, keys\n",
"\n",
"# List files\n",
"npz_files = sorted(NPZ_ROOT.rglob(\"*.npz\"))\n",
"print(\"Found npz files:\", len(npz_files))\n",
"print(\"Example:\", npz_files[0] if npz_files else \"NONE\")\n",
"\n",
"# Pick one (edit index or set by name)\n",
"case_path = npz_files[0] # <-- change to inspect a specific file\n",
"ct, rex, tot, keys = load_npz_case(case_path)\n",
"\n",
"print(\"Loaded:\", case_path.name)\n",
"print(\"Keys:\", keys)\n",
"print(\"CT:\", ct.shape, ct.dtype, f\"min={ct.min():.3f}, max={ct.max():.3f}\")\n",
"print(\"Rex:\", None if rex is None else (rex.shape, rex.dtype, f\"channels={rex.shape[0]}\"))\n",
"print(\"Tot:\", None if tot is None else (tot.shape, tot.dtype))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1d10b70",
"metadata": {},
"outputs": [],
"source": [
"def choose_rex_channel(rex_arr: np.ndarray):\n",
" \"\"\"\n",
" Returns (best_channel_index, counts_per_channel)\n",
" counts = number of voxels > 0 in each channel\n",
" \"\"\"\n",
" if rex_arr is None:\n",
" return None, None\n",
" counts = (rex_arr > 0).reshape(rex_arr.shape[0], -1).sum(axis=1)\n",
" best = int(np.argmax(counts))\n",
" return best, counts\n",
"\n",
"rex_ch, rex_counts = choose_rex_channel(rex)\n",
"if rex is not None:\n",
" print(\"Top 10 ReX channels by voxel count:\")\n",
" top = np.argsort(-rex_counts)[:10]\n",
" for i in top:\n",
" print(f\" ch={int(i):4d} voxels={int(rex_counts[i])}\")\n",
" print(\"Auto-selected channel:\", rex_ch)\n",
"else:\n",
" print(\"No ReX mask in this NPZ.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c75853f",
"metadata": {},
"outputs": [],
"source": [
"def mask_edges_2d(m2d: np.ndarray) -> np.ndarray:\n",
" \"\"\"Thin-ish edge for 2D mask.\"\"\"\n",
" m2d = (m2d > 0)\n",
" if not _HAS_SCIPY:\n",
" return m2d.astype(np.uint8) # fallback: filled mask\n",
" er = binary_erosion(m2d)\n",
" return (m2d ^ er).astype(np.uint8)\n",
"\n",
"def top_slices_by_area(mask_3d: np.ndarray, topk: int = 8):\n",
" \"\"\"\n",
" mask_3d: (D,H,W) boolean/int\n",
" returns list of axial slice indices with largest mask area\n",
" \"\"\"\n",
" areas = (mask_3d > 0).sum(axis=(1,2))\n",
" idx = np.argsort(-areas)[:topk]\n",
" return [int(i) for i in idx if areas[i] > 0], areas\n",
"\n",
"# Build binary masks for display\n",
"ct_vol = ct[0] # (D,H,W)\n",
"rex_mask = None\n",
"if rex is not None:\n",
" rex_mask = (rex[rex_ch] > 0) # (D,H,W)\n",
"\n",
"tot_mask = None\n",
"if tot is not None:\n",
" tot_mask = (tot[0] > 0) # (D,H,W)\n",
"\n",
"if rex_mask is not None:\n",
" idxs, areas = top_slices_by_area(rex_mask, topk=10)\n",
" print(\"Top axial slices by ReX area:\", idxs[:10])\n",
"else:\n",
" print(\"No ReX mask to suggest slices.\")\n",
"\n",
"if tot_mask is not None:\n",
" idxs2, areas2 = top_slices_by_area(tot_mask, topk=10)\n",
" print(\"Top axial slices by TotalSeg area:\", idxs2[:10])\n",
"else:\n",
" print(\"No TotalSeg mask to suggest slices.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "683579a8",
"metadata": {},
"outputs": [],
"source": [
"def show_axial_grid(ct_vol, rex_mask=None, tot_mask=None, slice_indices=None, rex_title=\"ReX\", tot_title=\"TotalSeg\"):\n",
" \"\"\"\n",
" ct_vol: (D,H,W) float\n",
" rex_mask / tot_mask: (D,H,W) bool\n",
" slice_indices: list[int]\n",
" \"\"\"\n",
" if slice_indices is None or len(slice_indices) == 0:\n",
" slice_indices = [ct_vol.shape[0] // 2]\n",
"\n",
" n = len(slice_indices)\n",
" fig, axes = plt.subplots(nrows=n, ncols=3, figsize=(12, 4*n))\n",
" if n == 1:\n",
" axes = np.array([axes])\n",
"\n",
" for r, d in enumerate(slice_indices):\n",
" ct2d = ct_vol[d]\n",
"\n",
" # Panel 1: CT\n",
" ax = axes[r, 0]\n",
" ax.imshow(ct2d, cmap=\"gray\", origin=\"lower\")\n",
" ax.set_title(f\"CT (axial d={d})\")\n",
" ax.axis(\"off\")\n",
"\n",
" # Panel 2: CT + ReX\n",
" ax = axes[r, 1]\n",
" ax.imshow(ct2d, cmap=\"gray\", origin=\"lower\")\n",
" if rex_mask is not None:\n",
" e = mask_edges_2d(rex_mask[d])\n",
" ax.imshow(e, cmap=\"Reds\", alpha=0.7, origin=\"lower\")\n",
" ax.set_title(f\"CT + {rex_title}\")\n",
" ax.axis(\"off\")\n",
"\n",
" # Panel 3: CT + TotalSeg\n",
" ax = axes[r, 2]\n",
" ax.imshow(ct2d, cmap=\"gray\", origin=\"lower\")\n",
" if tot_mask is not None:\n",
" e = mask_edges_2d(tot_mask[d])\n",
" ax.imshow(e, cmap=\"Blues\", alpha=0.6, origin=\"lower\")\n",
" ax.set_title(f\"CT + {tot_title}\")\n",
" ax.axis(\"off\")\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"# Choose slices to visualize (prefer slices with ReX content if present)\n",
"if rex_mask is not None:\n",
" slices, _ = top_slices_by_area(rex_mask, topk=3)\n",
" if len(slices) == 0:\n",
" slices = [ct_vol.shape[0]//2]\n",
"else:\n",
" slices = [ct_vol.shape[0]//2]\n",
"\n",
"show_axial_grid(ct_vol, rex_mask=rex_mask, tot_mask=tot_mask, slice_indices=slices[:3],\n",
" rex_title=f\"ReX(ch={rex_ch})\", tot_title=\"TotalSeg\")\n"
]
}
],
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|