Deep Learning Fundamentals and Health Applications

 02/04/2025 (11-13)

Deep Learning Fundamentals I

deep neural networks; gradient issues; activation functions; normalization and regularization; optimization

Deep Learning Fundamentals II

neural autoencoders; unsupervised deep learning; autoencoding tasks in healthcare (anomaly detection, compression, denoising)

[6] Survey on autoencoders

[7,8] Surveys on autoencoders use in healthcare

Convolutional Neural Networks I

Introduction to medical imaging; basic CNN elements; 

Convolutional Neural Networks II

CNN architectures; medical imaging tasks

Additional readings

[9] Augmentation techniques for medical imaging

[10] Survival analysis with CNNs

[11] Large-scale medical image segmentation dataset and application

Software

nnU-net - Well engineered framework for low-code medical segmentation tasks

Lab 8: Prova in itinere

AI Meets Psychiatry: fMRI-Based Multi-Disorder Diagnosis - Guest Lecture by Elisa Ferrari (CEO, Quantabrain)

QuantaBrain is a startup developing AI models for the diagnosis and characterization of psychiatric disorders using a short resting-state functional MRI scan, a complex 4D dataset influenced by numerous variables. In this seminar, the QuantaBrain team will present their neural network architecture, which combines adversarial learning with optical flow. They will share the technical challenges they encountered while training this network and scaling it from one to eleven disorders. Finally, participants will get an exclusive preview of the research platform they are currently building, designed to support scientists working on psychiatric disorders.

Lab Tutorial 9: Deep Neural Networks for image classification

Lab Tutorial 10: Deep Neural Networks for image segmentation

Spring Break: No Lectures