From the 1 of 133 papers with an AI index.
113 citations
- California Institute of TechnologyUS35 papers
- Centre National de la Recherche ScientifiqueFR28 papers
- Cornell UniversityUS27 papers
- University of ChicagoUS22 papers
- Princeton UniversityUS21 papers
- University of California, BerkeleyUS21 papers
- Fermi National Accelerator LaboratoryUS20 papers
- Université Paris-SaclayFR20 papers
- Massachusetts Institute of TechnologyUS18 papers
- University of CambridgeGB18 papers
- University of Maryland, College ParkUS18 papers
- Texas A&M UniversityUS17 papers
6 papers · 1 filter
GazeVaLM: A Multi-Observer Eye-Tracking Benchmark for Evaluating Clinical Realism in AI-Generated X-Rays
David Wong, Zeynep Isik, Bin Wang +22
We introduce GazeVaLM, a public eye-tracking dataset for studying clinical perception during chest radiograph authenticity assessment. The dataset comprises 960 gaze recordings fro…
What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric
Mohamed Amine Kerkouri, Marouane Tliba, Bin Wang +3
Scanpath similarity metrics are central to eye-movement research, yet existing methods predominantly evaluate spatial and temporal alignment while neglecting semantic equivalence b…
Handling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning
Ha-Hieu Pham, Hai-Dang Nguyen, Thanh-Huy Nguyen +4
Chest X-Ray (CXR) classification in clinical practice is often limited by imperfect supervision, arising from (i) extreme long-tailed multi-label disease distributions and (ii) mis…
Advancing Limited-Angle CT Reconstruction Through Diffusion-Based Sinogram Completion
Jiaqi Guo, Santiago Lopez-Tapia, Aggelos K. Katsaggelos
Limited Angle Computed Tomography (LACT) often faces significant challenges due to missing angular information. Unlike previous methods that operate in the image domain, we propose…
DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization
Thanh-Huy Nguyen, Hoang-Thien Nguyen, Vi Vu +6
The limited availability of annotated data in medical imaging makes semi-supervised learning increasingly appealing for its ability to learn from imperfect supervision. Recently, t…
Caption-Driven Explainability: Probing CNNs for Bias via CLIP
Patrick Koller, Amil V. Dravid, Guido M. Schuster +1
Robustness has become one of the most critical problems in machine learning (ML). The science of interpreting ML models to understand their behavior and improve their robustness is…