3 papers
cs.LG2026
T-MoXAI: A Hierarchical Explainability Framework for Temporal Multimodal Data
Ali Inha, Mo Vali, Saaliha Vali +2
Artificial Intelligence (AI) models for temporal multimodal data have potential in healthcare and agriculture, but their opacity can limit trust and adoption. We introduce T-MoXAI…
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…