3 papers
cs.CL2026
A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models
Michail Mamalakis, Tiago Azevedo, Cristian Cosentino +5
Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer disease, where early and trustworthy pre…
eess.IV2026
RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis
Chris Tomy, Mo Vali, David Pertzborn +9
Histopathology, the current gold standard for cancer diagnosis, involves the manual examination of tissue samples after chemical staining, a time-consuming process requiring expert…
cs.CV2024
Artificial Intelligence-powered fossil shark tooth identification: Unleashing the potential of Convolutional Neural Networks
Andrea Barucci, Giulia Ciacci, Pietro Liò +7
All fields of knowledge are being impacted by Artificial Intelligence. In particular, the Deep Learning paradigm enables the development of data analysis tools that support subject…