HamVision: Hamiltonian dynamics as inductive bias for medical image analysis
Year: 2026
Type: article
Abstract: input, a 4× parameter and 7× FLOPs reduction relative to the prior state-of-the-art MedKAFormer-T, and leads or ties it on 8 of 9 datasets at 3-seed precision, with margins up to ＋17.76 percentage points macro F1 on DermaMNIST and ＋8.30 percentage points top-1 on OCTMNIST. On the smallest binary benchmark, BreastMNIST (546 training images), HamCls trails MedKAFormer-T; the gap is consistent with the small-data regime. Diagnostic measurements show that momentum carries an interior > boundary > exterior activity gradient on lesion-segmentation tasks, and that the energy map concentrates on the class-discriminative structure of every classification modality without any localisation supervision. An ablation study confirms that the bottleneck contributes signal that a depth-matched conventional convolutional block cannot replicate. Code, configurations, and trained models will be released on acceptance.
Source: Medical Image Analysis
Author: Mohamed A. Mabrok
Institution: Qatar University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,170
Funder: Qatar University
Award: G7551687063
OpenAlex ID: W7140165815