BiomedSciAI/fuse-med-ml
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
PyTorchcomputer visiondata augmentationdeep learningmachine learningmodel training
xinwxin
PROFILE
Xinwxin
Developed a new 3D medical imaging model backbone and training enhancements for the BiomedSciAI/fuse-med-ml repository, focusing on improved segmentation and classification of medical images. Leveraged PyTorch and deep learning techniques, integrating EfficientNet and UNet architectures to boost model performance. Introduced self-supervised learning methods, including DINO and Masked Autoencoder, to accelerate training and enhance accuracy. Updated data handling and augmentation pipelines to support both 2D and 3D datasets, enabling flexible model training and evaluation. Completed master merge cleanup and resolved integration issues, contributing to repository stability and deployment readiness. All work was implemented in Python with a focus on reproducibility.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,230
Activity Months1
Your Network
4 peopleShared Repositories
4
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for BiomedSciAI/fuse-med-ml: Delivered a new 3D medical imaging model backbone and training enhancements, enabling improved 3D segmentation and classification. Integrated self-supervised learning (DINO and Masked Autoencoder) to boost training efficiency and accuracy. Updated data handling and augmentation to support both 2D and 3D datasets, enabling flexible model training and evaluation. Completed final master merge cleanup and fixed oai example integration (#413), enhancing repository stability and deployment readiness.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for BiomedSciAI/fuse-med-ml: Delivered a new 3D medical imaging model backbone and training enhancements, enabling improved 3D segmentation and classification. Integrated self-supervised learning (DINO and Masked Autoencoder) to boost training efficiency and accuracy. Updated data handling and augmentation to support both 2D and 3D datasets, enabling flexible model training and evaluation. Completed final master merge cleanup and fixed oai example integration (#413), enhancing repository stability and deployment readiness.
March 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PyTorchcomputer visiondata augmentationdeep learningmachine learningmodel training
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