Speaker: Simone Callegarin
17 Marzo 2026 | 14:15
DEIB, Sala Seminari "A. Alario" (Ed. 21)
Per maggiori informazioni: Silvia Cascianelli | silvia.cascianelli@polimi.it
Sommario
Tuesday, March 17th, 2026 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.The seminar will be held by Simone Callegarin, master student in Computer Science Engineering, on the following subject: "3D Convolutional Neural Network for assessment of skin biopsy innervation".
Small Fibre Neuropathy (SFN) is a neurological disorder involving small somatic and autonomic nerve fibres, leading to chronic neuropathic pain and reduced quality of life. Since these fibres cannot be assessed by routine nerve conduction studies, diagnosis relies on skin biopsy with manual quantification of intraepidermal nerve fibre density (IENFD). Although considered the reference standard, this method is time-consuming and operator- dependent, highlighting the need for more efficient and objective approaches. In this context, deep learning techniques, particularly Convolutional Neural Networks (CNNs), offer a promising solution for automated and reproducible analysis. This work presents a fully automated framework for IENFD quantification from immunofluorescence skin biopsy images using three-dimensional CNNs (3D CNNs). In collaboration with the Fondazione I.R.C.C.S. Istituto Neurologico Carlo Besta, a dedicated dataset was created from 60 biopsies collected from 20 patients. A comprehensive preprocessing pipeline was developed to cover the entire workflow, from raw volumetric data to patient-level diagnostic inference. It was designed to standardize and facilitate the manual annotation process through orientation correction, denoising, and signal enhancement, and also to optimize model performance by reducing irrelevant variability and refining the input data for deep learning training. A 3D CNN regression model was trained to directly predict intraepidermal fibre counts from three-dimensional fields of view (FOVs). The training strategy included cross-validation, customized loss, and ablation studies to assess the contribution of methodological components. The proposed model achieves accurate fibre count prediction at the FOV level and provides clinically reliable IENFD estimates at the biopsy level. Overall, this work demonstrates the feasibility and effectiveness of a fully automated volumetric deep learning framework for IENFD quantification, representing a concrete step toward integrating artificial intelligence into the diagnostic workflow of SFN.