From the 1 of 6 linked papers with an AI index.
6 papers
When More References Hurt: Contamination-Aware DINOv2 Memory Banks for Few-Shot Steel Defect Detection
Hannaneh Kalantari, Hannaneh Kalantary, Javad Khoramdel
Patch-memory anomaly detectors assume that their reference bank is normal, an assumption that is difficult to guarantee when additional industrial images are unverified. We study w…
Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening
Javad Khoramdel, Farhad Hoseyni, Amirhossein Nikoofard
The paper introduces a lightweight method that adapts a frozen vision model using learnable prompt tokens and an adaptive focal loss to detect mild cognitive impairment from drawin…
PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving
Yasamin Borhani, Taylor Mordan, Yihan Wang +3
Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applicatio…
CT Scans As Video: Efficient Intracranial Hemorrhage Detection Using Multi-Object Tracking
Amirreza Parvahan, Mohammad Hoseyni, Javad Khoramdel +1
Automated analysis of volumetric medical imaging on edge devices is severely constrained by the high memory and computational demands of 3D Convolutional Neural Networks (CNNs). Th…
Hemorica: A Comprehensive CT Scan Dataset for Automated Brain Hemorrhage Classification, Segmentation, and Detection
Kasra Davoodi, Mohammad Hoseyni, Javad Khoramdel +5
Timely diagnosis of Intracranial hemorrhage (ICH) on Computed Tomography (CT) scans remains a clinical priority, yet the development of robust Artificial Intelligence (AI) solution…
Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset
Z. Rafati, M. Hoseyni, J. Khoramdel +1
Explainable Artificial Intelligence (XAI) has become an essential component of medical imaging research, aiming to increase transparency and clinical trust in deep learning models.…