activity
20212026
most citedA transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies

9 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

physics.optics2026

A unified perspective on wavelength selection for molecular composition inference from diffuse spectroscopy

Kevin Scibilia, Marcel Rosier, Luca Giannoni +6

Optical monitoring of living tissue targeting quantification of molecular composition is an active area of research. In the case of broadband spectroscopy, one can attempt to extra…

physics.med-ph2024★ 9 cited

A transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies

Luca Giannoni, Marta Marradi, Kevin Scibilia +11

Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long times required for the labora…

physics.bio-ph2023

Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue

Ivan Ezhov, Kevin Scibilia, Luca Giannoni +8

Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fa…

cs.CE2022

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Ivan Ezhov, Marcel Rosier, Lucas Zimmer +12

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical them…

physics.med-ph2021★ 1 cited

Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

Ivan Ezhov, Kevin Scibilia, Katharina Franitza +12

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.…