Editing Computational Pathology (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''WSI classification with CLAM (MIL):'''
<syntaxhighlight lang="python">
import torch
import torch.nn as nn
import torch.nn.functional as F

class Attn_Net_Gated(nn.Module):
    """Gated attention network for MIL aggregation."""
    def __init__(self, L=1024, D=256, dropout=0.25):
        super().__init__()
        self.attention_a = nn.Sequential(nn.Linear(L, D), nn.Tanh(), nn.Dropout(dropout))
        self.attention_b = nn.Sequential(nn.Linear(L, D), nn.Sigmoid(), nn.Dropout(dropout))
        self.attention_c = nn.Linear(D, 1)

    def forward(self, x):
        a = self.attention_a(x)
        b = self.attention_b(x)
        A = self.attention_c(a * b)  # Gated attention scores
        return A, x  # (N, 1), (N, L)

class CLAM_SB(nn.Module):
    """CLAM single-branch for binary WSI classification."""
    def __init__(self, feature_dim=1024, n_classes=2, dropout=0.25):
        super().__init__()
        self.attention_net = Attn_Net_Gated(L=feature_dim, D=256, dropout=dropout)
        self.classifiers = nn.Linear(feature_dim, n_classes)
        self.instance_classifier = nn.Linear(feature_dim, 2)  # For instance-level clustering

    def forward(self, h):
        # h: (N, feature_dim) — patch embeddings from pre-trained feature extractor
        A, h = self.attention_net(h)
        A = F.softmax(A, dim=0).transpose(0, 1)  # Softmax over patches: (1, N)
        M = torch.mm(A, h)  # Weighted aggregation: (1, feature_dim)
        logits = self.classifiers(M)  # Slide-level prediction
        Y_hat = torch.argmax(logits, dim=1)
        Y_prob = F.softmax(logits, dim=1)
        return logits, Y_prob, Y_hat, A  # A contains attention scores for visualization

# Feature extraction pipeline
# 1. Segment tissue from background (Otsu thresholding)
# 2. Extract non-overlapping 256×256 patches at 20× magnification
# 3. Extract features using pathology foundation model (UNI, CONCH, ResNet50-ImageNet)
# 4. Feed patch features to CLAM for WSI-level prediction

# Using UNI (pre-trained ViT on 100K pathology images)
# import timm
# uni = timm.create_model("hf_hub:MahmoodLab/uni", pretrained=True)
</syntaxhighlight>

; Computational pathology tools
: '''WSI viewing''' → QuPath (open-source), Aperio ImageScope, SlideViewer
: '''MIL frameworks''' → CLAM (GitHub), TransMIL, DTFD-MIL
: '''Foundation models''' → UNI, CONCH (Mahmood Lab), Prov-GigaPath (Microsoft/Providence)
: '''Cell segmentation''' → HoverNet, StarDist, CellViT, CellPose
: '''Commercial AI''' → Paige, PathAI, Aiforia, Ibex Medical Analytics
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">