From the 2 of 18 linked papers with an AI index.
6 papers · 1 filter
Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari +2
The Segment Anything Model (SAM) exhibits strong zero-shot performance on natural images but suffers from domain shift and overconfidence when applied to medical volumes. We propos…
Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models
Behraj Khan, Tahir Syed
Confidence calibration is an emerging challenge in real-world decision systems based on foundations models when used for downstream vision classification tasks. Due to various reas…
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift
Behraj Khan, Tahir Qasim Syed, Nouman M. Durrani +3
Foundation models like CLIP and SAM have advanced computer vision and medical imaging via low-shot transfer learning, aiding CADD with limited data. However, their deployment faces…
Confidence-calibrated covariate shift correction for few-shot classification in Vision-Language Models
Behraj Khan, Rizwan Qureshi, Nouman Muhammad Durrani +1
Since the establishment of vision-language foundation models as the new mainstay in low-shot vision classification tasks, the question of domain generalization arising from insuffi…
Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions
Annayah Usman, Abdul Haseeb, Tahir Syed
Segmentation of Hypoxic-Ischemic Encephalopathy (HIE) lesions in neonatal MRI is a crucial but challenging task due to diffuse multifocal lesions with varying volumes and the limit…
Latents of latents to delineate pixels: hybrid Matryoshka autoencoder-to-U-Net pairing for segmenting large medical images in GPU-poor and low-data regimes
Tahir Syed, Ariba Khan, Sawera Hanif
Medical images are often high-resolution and lose important detail if downsampled, making pixel-level methods such as semantic segmentation much less efficient if performed on a lo…