Design and development of machine learning models to analyze clinical images for early diagnosis diseases
Tesi esterna in azienda
Parole chiave ARTIFICIAL INTELLIGENCE, DECISION SUPPORT, MACHINE LEARNING, MEDICAL IMAGING, NEURAL NETWORKS
Riferimenti SANTA DI CATALDO, EDOARDO PATTI
Riferimenti esterni Daniele Conti (firstname.lastname@example.org)
Gruppi di ricerca DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ELECTRONIC DESIGN AUTOMATION - EDA, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ICT4SS - ICT FOR SMART SOCIETIES
Tipo tesi SPERIMENTALE IN AZIENDA
Descrizione SynDiag in an Italian startup born to enable early diagnosis of ovarian cancer with artificial intelligence applied to medical imaging.
Ovarian cancer is a pathology presenting high mortality due to late diagnosis: 75% of clinical cases are detected when already developed and with survival rate at 30%. Performing an early diagnosis would increase the survival probability as high as 90%. For such a reason SynDiag wants to equip al gynecologists with a medical device based on AI that speeds up the diagnostic process.
SynDiag is a young team composed of researchers, medical doctors and entrepreneurs, hosted at I3P – Incubator of Politecnico di Torino. We collaborate with hospitals in Italy and Israel.
The thesis here proposed, in the field of machine learning, is focused on the development of neural network models, such as multi layer perceptrons, deep convolutional neural networks, region-based convolutional neural networks.
The thesis will focus on:
- Analysis of neural networks algorithms and available learning protocols
- Training of neural networks with medical images data
- Testing and evaluation of clinical accuracy of trained NN models
Scadenza validita proposta 15/02/2022 PROPONI LA TUA CANDIDATURA