Histopathology Image Classification — 4th / 170

December 1, 2025 · 1 min read

Image classification of molecular structures in tissue images for the Artificial Neural Networks & Deep Learning course at Politecnico di Milano — ranked 4th of 170 teams on a hidden test set.

  • Backbone: transfer learning with a ResNet-50 pre-trained via RetCCL for histopathology-specific feature extraction.
  • Pipeline: reproducible PyTorch Lightning training with WeightedRandomSampler for class imbalance, AdamW, label smoothing, cosine annealing, and rotation/flip/color-jitter augmentation.

Python · PyTorch · PyTorch Lightning · CNNs

Deep Learning Transfer Learning PyTorch Computer Vision
Lorenzo Ortolani
Authors
Lorenzo Ortolani (he/him)
Roboticist · Physical AI & VLA for Humanoids
I build Physical AI — robots that perceive, reason, and act in the real world. My focus is advancing Vision-Language-Action (VLA) policies so that a single humanoid can learn a diverse library of skills and choose the right one for whatever industrial task it faces, generalizing far beyond a single demonstration. I co-founded TalOS Robotics AI to put this in operators’ hands, and I work across the full stack: locomotion and navigation, learned manipulation, sim-to-real, and the agent tooling that wraps it all together.