Speakers: Sara Resta, Luca Zanotto
9 Dicembre2025 | 14:15
DEIB, Sala Seminari "A. Alario" (Ed. 21)
Per maggiori informazioni: Silvia Cascianelli | silvia.cascianelli@polimi.it
Sommario
Tuesday, December 9, 2025 at 2:15 pm a new appointment of Data Science Seminars: Bioinformatics focus will take place in DEIB "Alessandra Alario" Seminar Room (Building 21) organized by the Data Science for Bioinformatics group.These Thesis Rehearsal will be held by Sara Resta and Luca Zanotto on the following subjects:
"Convolutional Neural Networks for [18f]FDG PET imaging-based prediction of clinical relapse in patients with Takayasu Arteritis" - Sara Resta
Takayasu arteritis is a rare chronic inflammatory disease that mainly affects the aorta and its major branches. Although [18F]FDG PET is the imaging technique with the highest sensitivity for detecting vascular lesions, its role in monitoring disease status remains debated. The highly heterogeneous and sparse distribution of lesions across multiple vascular sites poses challenges in delineating volumes of interest and limits the application of quantitative analysis techniques commonly used in oncology, such as SUV (Standardized Uptake Value) metrics and radiomics. This study investigates the use of Convolutional Neural Networks (CNNs) applied directly to [18F]FDG PET scans to predict patient relapse within 12 months after imaging. A new liver-based standardization approach was optimized to minimize biases during training. As it is not known how the information about the likelihood of a patient to experience flare is encoded in the scans, CNNs were first trained to classify scans according to the presence of pathological uptake in the arteries. Then, these models were leveraged for flare prediction through transfer learning. The model EfficientNetB0 showed promise in predicting complete remission potentially allowing physicians to identify patients that can avoid aggressive therapies and stringent follow-ups.
"Deep Learning Approaches for Alpha-Synucleinopathies Classification Using Skin Biopsy Images"- Luca Zanotto
Parkinson’s Disease (PD) and Multiple System Atrophy (MSA) are neurodegenerative a-synucleinopathies that share early symptomatology but require distinct treatments. Recent evidence highlights that cutaneous sweat-gland synaptic innervation, assessed via skin biopsies, may offer a cost-effective and minimally invasive biomarker for differentiation. This study develops a deep learning pipeline analyzing confocal microscopy images of these glands to distinguish PD from MSA. Specifically, images were used to train several deep learning architectures, evaluated across different training strategies. Additionally, explainability methods were applied to better understand the decision-making process of the models. Results demonstrate the efficacy of these architectures, achieving promising performance, especially in the clinically challenging task of differentiating the MSA-parkinsonian subtype (MSA-P) from PD.