Francesco Di Gangi
Few-shot Learning in Vision Transformers for Skin Cancer Semantic Segmentation.
Rel. Tatiana Tommasi. Politecnico di Torino, Master of science program in Computer Engineering, 2024
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (21MB) | Preview |
|
|
Archive (ZIP) (Documenti_allegati)
- Other
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (1kB) |
Abstract
Skin cancer is one of the most prevalent and potentially life-threatening diseases, characterized by aberrant skin cell proliferation, mostly caused by DNA damage due to exposure to ultraviolet (UV) radiation from the sun or other sources, like tanning beds. There are several types of skin cancer, such as melanoma, squamous cell carcinoma, and basal cell carcinoma, each with unique traits and implications for treatment and diagnosis. Timely detection of skin cancer is fundamental to maximize the probability of successful treatment. In this regard, computer-assisted diagnosis plays a crucial role and is supported by the automated analysis of images through segmentation. Segmentation effectively recognizes and outlines regions of interest in dermoscopic images, aiding healthcare practitioners in precisely identifying and assessing lesions.
This enables early diagnosis and the tracking of skin changes over time, ultimately facilitating prompt medical intervention
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modify record (reserved for operators) |
