Colon Histopathology AI Classifier
End-to-end ML research pipeline automating colon cell cancer detection and cell-type classification from 27×27 RGB histopathology patches. Rigorous patient-level evaluation on unseen clinical data.
Exceeded diagnostic targets (≥0.90 cancer detection, ≥0.60 cell-type) on genuinely unseen patients — not just new images from seen patients.
Designed, trained, and evaluated 12 distinct models across two diagnostic tasks using classical ML (SVM, Random Forest) and custom PyTorch CNNs.
Implemented Test-Time Augmentation (TTA), dynamic learning rate scheduling, and per-epoch data augmentation.
Conducted controlled transfer learning experiments with CIFAR-10 pretraining, documenting negative transfer between natural-image and medical domains.
Architected strict patient-level train/validation/test splits preventing data leakage from staining and scanner artifacts.
Achieved 0.91 Macro F1 on binary cancer detection and 0.75 Macro F1 on 4-class cell-type classification on held-out unseen patients.
Performed comprehensive EDA using PCA, t-SNE, and K-Means clustering to analyze latent space and validate decision boundaries.
Patient-level evaluation preventing clinical data leakage
12 models across cancer detection and cell-type classification
CNN + transfer learning + TTA optimization
Macro F1 as primary metric for class imbalance
Controlled experiments isolating augmentation and ensemble effects