From the 2 of 17 linked papers with an AI index.
17 papers
CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation
Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari +1
The paper introduces CalTwin, a lightweight regularization that combines a Fisher‑information‑based shift penalty with a confidence‑misalignment penalty to make GRU‑based medical w…
PIcsC: Partitioning-Induced Covariate Shift Correction
Behraj Khan, Behroz Mirza, Syed Ahmad Chan Bukhari +1
The paper introduces PIcsC, a Fisher information‑based regularization method that corrects covariate shift caused by data partitioning in both centralized (e.g., cross‑validation)…
Technical note on Sequential Test-Time Adaptation via Martingale-Driven Fisher Prompting
Behraj Khan, Tahir Qasim Syed
We present a theoretical framework for M-FISHER, a method for sequential distribution shift detection and stable adaptation in streaming data. For detection, we construct an expone…
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…