AI in Computational Pathology

Whole Slide Image (WSI) Analysis

Attention-Based Multiple Instance Learning

import torch
import torch.nn as nn

class WSIClassifier(nn.Module):
    def __init__(self, feature_dim=512, n_classes=2):
        super().__init__()
        self.feature_extractor = nn.Sequential(
            nn.Linear(feature_dim, 256), nn.ReLU(),
            nn.Dropout(0.3), nn.Linear(256, 128))
        self.attention = nn.Linear(128, 1)
        self.classifier = nn.Linear(128, n_classes)

    def forward(self, patches):
        features = self.feature_extractor(patches)
        attn = torch.softmax(self.attention(features), dim=0)
        bag_repr = (attn * features).sum(dim=0)
        return self.classifier(bag_repr)

Mitosis Detection

Density Estimation

Tumor Grading

IHC Biomarker Quantification