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To address the prohibitive deployment overhead of massive architectures in clinical imaging, LightMedSeg-ISLES establishes an ultra-compact 1.26-million-parameter pipeline for T1-weighted stroke lesion segmentation on the ISLES'26 benchmark. Evaluated on a 146-case held-out cohort, the model achieves a 0.618 mean Dice and 0.599 lesion-wise F1, retaining 97.5% of a 102.35M-parameter nnU-Net ResEnc-L's Dice score while surpassing it by 0.055 on lesion detection. These findings demonstrate that aggressive data augmentation and extended training schedules can bridge the capacity gap against models over 80× larger while reducing per-patch FLOPs by 4.7×.
Shrinking a 102M-parameter nnU-Net by 81× costs barely 2.5% in segmentation Dice while actually boosting lesion-level detection F1 by over 5 points.
Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present LightMedSeg-ISLES, a 1.26-million-parameter pipeline for T1-weighted stroke lesion segmentation in ISLES'26. On a 146-case held-out cohort, flip test-time augmentation produces 0.618 mean Dice and 0.599 lesion-wise F1. A 102.35-million-parameter nnU-Net ResEnc-L produces 0.634 Dice and 0.544 lesion-wise F1 after size filtering. LightMedSeg therefore retains 97.5\% of nnU-Net's Dice with 81.4$\times$ fewer parameters while improving lesion-wise F1 by 0.055. Its four-pass TTA operating point requires 4.7$\times$ fewer FLOPs per standardized patch than nnU-Net. It also slightly exceeds filtered UNETR++ and nnFormer. Longer training and stronger augmentation add 0.0358 Dice without increasing capacity, establishing a strong single-checkpoint alternative to much larger models.