hoangtung386/brain-stroke-segmentation
Brain Stroke Segmentation in Medical Images: Advanced LCNN (Lightweight CNN) architecture for automatic detection and precise segmentation of ischemic or hemorrhagic lesions in medical scans.
What's novel
Brain Stroke Segmentation in Medical Images: Advanced LCNN (Lightweight CNN) architecture for automatic detection and precise segmentation of ischemic or hemorrhagic lesions in medical scans.
Code Analysis
13 files read · 4 roundsBrain stroke lesion segmentation using a custom dual-path LCNN architecture that combines a Symmetry Enhanced Attention Network (SEAN) exploiting bilateral brain symmetry with a ResNeXt50 global semantic path, trained with a combined Dice+CE+alignment loss.
Strengths
The SymmetryEnhancedAttention module is a genuinely novel approach to medical image segmentation that leverages the inherent bilateral symmetry of the brain to identify anomalies by comparing contralateral features. The architecture is well-structured with clear separation between local (3D SEAN) and global (ResNeXt) paths, and the loss function thoughtfully combines segmentation, symmetry, regularization, and edge-consistency terms.
Weaknesses
The code reveals significant training stability struggles (AMP disabled, aggressive gradient clipping, pervasive NaN checks, Vietnamese debug comments) suggesting the model is difficult to train reliably. There are no tests, a duplicated save_checkpoint method, hardcoded cardiac class names in the validator that don't match the stroke domain, and the validator's DiceMetric aggregation logic is fragile.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
126
Contributors
2
Files
42
Active weeks
4
Repository
Language
Python
Stars
2
Forks
0
License
MIT