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import os
# import h5py
import numpy as np
# import pandas as pd
# from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
# from torch.utils.data import Dataset, DataLoader
from monai.networks.nets import SegResNet
from huggingface_hub import hf_hub_download
# from tqdm.notebook import tqdm, trange
# class EmbeddingsDataset(Dataset):
# """Helper class to load and work with the stored embeddings"""
# def __init__(self, embeddings_path, metadata_path, transform=None):
# """
# Initialize the dataset
# Args:
# embeddings_path: Path to the directory containing H5 embedding files
# metadata_path: Path to the directory containing metadata files
# transform: Optional transforms to apply to the data
# """
# self.embeddings_path = embeddings_path
# self.metadata_path = metadata_path
# self.transform = transform
# self.master_metadata = pd.read_parquet(os.path.join(metadata_path, "master_metadata.parquet"))
# # Limit to data with labels
# self.metadata = self.master_metadata.dropna(subset=['label'])
# def __len__(self):
# return len(self.metadata)
# def __getitem__(self, idx):
# """Get embedding and label for a specific index"""
# row = self.metadata.iloc[idx]
# batch_name = row['embedding_batch']
# embedding_index = row['embedding_index']
# label = row['label']
# # Load the embedding
# h5_path = os.path.join(self.embeddings_path, f"{batch_name}.h5")
# with h5py.File(h5_path, 'r') as h5f:
# embedding = h5f['embeddings'][embedding_index]
# # Convert to PyTorch tensor
# embedding = torch.tensor(embedding, dtype=torch.float32)
# # Reshape for CNN input - we expect embeddings of shape (384,)
# # Reshape to (1, 384, 1, 1) for network input
# embedding = embedding.view(1, 384, 1)
# # Convert label to tensor (0=negative, 1=positive)
# label = torch.tensor(label, dtype=torch.long)
# if self.transform:
# embedding = self.transform(embedding)
# return embedding, label
# def get_embedding(self, file_id):
# """Get embedding for a specific file ID"""
# # Find the file in metadata
# file_info = self.master_metadata[self.master_metadata['file_id'] == file_id]
# if len(file_info) == 0:
# raise ValueError(f"File ID {file_id} not found in metadata")
# # Get the batch and index
# batch_name = file_info['embedding_batch'].iloc[0]
# embedding_index = file_info['embedding_index'].iloc[0]
# # Load the embedding
# h5_path = os.path.join(self.embeddings_path, f"{batch_name}.h5")
# with h5py.File(h5_path, 'r') as h5f:
# embedding = h5f['embeddings'][embedding_index]
# return embedding, file_info['label'].iloc[0] if 'label' in file_info.columns else None
class SelfSupervisedHead(nn.Module):
"""Self-supervised learning head for cancer classification
Since no coordinates or bounding boxes are available, this head focuses on
learning from the entire embedding through self-supervision.
"""
def __init__(self, in_channels, num_classes=2):
super(SelfSupervisedHead, self).__init__()
self.conv = nn.Conv2d(in_channels, 128, kernel_size=1)
self.bn = nn.BatchNorm2d(128)
self.relu = nn.ReLU(inplace=True)
self.global_pool = nn.AdaptiveAvgPool2d(1)
# Self-supervised projector (MLP)
self.projector = nn.Sequential(
nn.Linear(128, 256),
nn.BatchNorm1d(256),
nn.ReLU(inplace=True),
nn.Linear(256, 128)
)
# Classification layer
self.fc = nn.Linear(128, num_classes)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
x = self.global_pool(x)
x = x.view(x.size(0), -1)
# Apply projector for self-supervised learning
features = self.projector(x)
# Classification output
output = self.fc(features)
return output, features
class SelfSupervisedCancerModel(nn.Module):
"""SegResNet with self-supervised learning head for cancer classification"""
def __init__(self, num_classes=2):
super(SelfSupervisedCancerModel, self).__init__()
# Initialize SegResNet as backbone
# Modified to work with 1-channel input and small input size
self.backbone = SegResNet(
spatial_dims=2,
in_channels=1,
out_channels=2,
blocks_down=[3, 4, 23, 3],
blocks_up=[3, 6, 3],
upsample_mode="deconv",
init_filters=32,
)
# We know from the structure that the final conv layer outputs 2 channels
# Look at the print of self.backbone.conv_final showing Conv2d(8, 2, ...)
backbone_out_channels = 2
# Replace classifier with our self-supervised head
self.ssl_head = SelfSupervisedHead(backbone_out_channels, num_classes)
# Remove original classifier if needed
if hasattr(self.backbone, 'class_layers'):
self.backbone.class_layers = nn.Identity()
def forward(self, x, return_features=False):
# Run through backbone
features = self.backbone(x)
# Apply self-supervised head
output, proj_features = self.ssl_head(features)
if return_features:
return output, proj_features
return output
def load_model():
path = hf_hub_download(repo_id="Arpit-Bansal/Medical-Diagnosing-models", filename="cancer_detector_model.pth",
)
model = SelfSupervisedCancerModel(num_classes=2)
state_dict = torch.load(path, map_location=torch.device('cpu'))
model.load_state_dict(state_dict=state_dict)
return model.eval()
def classify(model, embedding):
"""Classify a single embedding using the trained model"""
# Ensure the model is in evaluation
embedding_tensor = torch.tensor(embedding, dtype=torch.float32).view(1, 1, 384, 1)
with torch.no_grad():
output = model(embedding_tensor)
probs = torch.softmax(output, dim=1)
predicted_class = torch.argmax(probs, dim=1).item()
confidence = probs[0, predicted_class].item()
prediction = "positive" if predicted_class == 1 else "negative"
return prediction, confidence
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