3 citations · 3 across the 19 of their papers we have counts for
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Technical note on: Zero-Training Feature-Space Alignment via Information Geometry
Behraj Khan, Tahir Qasim Syed, Syed Ahmad Chan Bukhari
Deep vision models often degrade under distribution shift. Test-time adaptation can improve robustness but typically requires iterative optimization, hyperparameter tuning, and mul…
CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation
Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari +1
Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting down…
PIcsC: Partitioning-Induced Covariate Shift Correction
Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari +1
Covariate shift across training-data partitions biases model selection and parameter estimation in cross-validation, lifelong learning, and federated learning. We propose \textit{P…
Extending 2D foundational DINOv3 representations to 3D segmentation of neonatal brain MR images
Annayah Usman, Behraj Khan, Tahir Qasim Syed
Precise volumetric delineation of hippocampal structures is essential for quantifying neurodevelopmental trajectories in pre-term and term infants, where subtle morphological varia…
Clinically-aligned ischemic stroke segmentation and ASPECTS scoring on NCCT imaging using a slice-gated loss on foundation representations
Hiba Azeem, Behraj Khan, Tahir Qasim Syed
Rapid infarct assessment on non-contrast CT (NCCT) is essential for acute ischemic stroke management. Most deep learning methods perform pixel-wise segmentation without modeling th…
Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptation
Tahir Qasim Syed, Behraj Khan
Often, constraints arise in deployment settings where even lightweight parameter updates e.g. parameter-efficient fine-tuning could induce model shift or tuning instability. We stu…