| KEYWORD |
Design and development of algorithms for noise reductions and automatic segmentation of tissues in ultrasound images
Thesis in external company
keywords ARTIFICIAL INTELLIGENCE, COMPUTER VISION, DECISION SUPPORT, MACHINE LEARNING, MEDICAL IMAGING, NEURAL NETWORKS
Reference persons SANTA DI CATALDO, EDOARDO PATTI
External reference persons Rosilari Bellacosa Marotti (rosilari.bellacosa@syndiag.ai), Daniele Conti (daniele.conti@syndiag.ai)
Research Groups DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ELECTRONIC DESIGN AUTOMATION - EDA, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ICT4SS - ICT FOR SMART SOCIETIES
Thesis type EXPERIMENTAL, IN COMPANY
Description 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 Computer Vision, is focused on the development of algorithms for noise reductions and automatic segmentation of tissues in ultrasound images.
The thesis will focus on:
- Analysis of available algorithms for noise reductions
- Analysis of available algorithms for quantization and segmentation
- Development of algorithms for identification of different morphological features
- Focus on development of solid-liquid and soild-solid interfaces detection algorithms
Deadline 18/10/2020
PROPONI LA TUA CANDIDATURA