187 citations
- University of PennsylvaniaUS2 papers
- ARAMIS: Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain sain et pathologiqueFR1 paper
- BC Cancer AgencyCA1 paper
- California University of PennsylvaniaUS1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Columbia UniversityUS1 paper
- Ege UniversityTR1 paper
- Foundation for Research and Technology HellasGR1 paper
- Icahn School of Medicine at Mount SinaiUS1 paper
- Institut du CerveauFR1 paper
- Institut national de recherche en sciences et technologies du numériqueFR1 paper
- Mayo Clinic in FloridaUS1 paper
8 papers
Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models
Nariman Naderi, Seyed Amir Ahmad Safavi-Naini, Thomas Savage +4
This study evaluated self-reported response certainty across several large language models (GPT, Claude, Llama, Phi, Mistral, Gemini, Gemma, and Qwen) using 300 gastroenterology bo…
Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Xinyue Hu, Zenan Sun, Yi Nian +6
Background: Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While…
Electroanatomic Mapping to determine Scar Regions in patients with Atrial Fibrillation
Jiyue He, Kuk Jin Jang, Katie Walsh +3
Left atrial voltage maps are routinely acquired during electroanatomic mapping in patients undergoing catheter ablation for atrial fibrillation. For patients, who have prior cathet…
A brief history of AI: how to prevent another winter (a critical review)
Amirhosein Toosi, Andrea Bottino, Babak Saboury +2
The field of artificial intelligence (AI), regarded as one of the most enigmatic areas of science, has witnessed exponential growth in the past decade including a remarkably wide a…
Improving J-divergence of brain connectivity states by graph Laplacian denoising
Tiziana Cattai, Gaetano Scarano, Marie-Constance Corsi +3
Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detec…
Semi-Supervised Deep Learning for Multi-Tissue Segmentation from Multi-Contrast MRI
Syed Muhammad Anwar, Ismail Irmakci, Drew A. Torigian +5
Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and rese…