arvindkrishna87/STAT390_SP25_CMIL
Apr 2025 – Jun 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data PreprocessingDeep LearningImage ClassificationKerasMachine LearningTensorFlow
RohanSubr
PROFILE
Rohansubr
Developed and enhanced end-to-end image analysis pipelines for the arvindkrishna87/STAT390_SP25_CMIL repository, focusing on histology data preprocessing, patch extraction, and deep learning-based classification. Leveraged Python, PyTorch, and TensorFlow to implement reproducible workflows, including CNN and AlexNet-style models, robust data augmentation, and custom dataset handling. Introduced SSIM-guided dynamic averaging to improve multi-stain image patch reliability and addressed cross-stain alignment challenges with threshold-based logic. Emphasized modularity and scalability, delivering well-documented Jupyter Notebooks and reusable tooling for model evaluation and robustness testing. The work established a foundation for automated, production-ready pathology analysis with minimal manual intervention and strong reproducibility.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
5
Lines of code
5,396
Activity Months3
Your Network
122 peopleSame Organization
@u.northwestern.edu114
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for arvindkrishna87/STAT390_SP25_CMIL: Delivered SSIM-guided dynamic averaging for multi-stain image patches. Introduced a dynamic averaging strategy that uses SSIM to decide whether Sox10 stained images should be included in the averaging with H&E and Melan. If SSIM is below threshold, only H&E and Melan are averaged; if SSIM meets threshold, all three (H&E, Melan, Sox10) are averaged. This enhances robustness of image analysis by handling misalignments in stain imaging and improves the reliability of downstream metrics. This work supports more automated, scalable pathology analysis with reduced need for manual corrections. Commits: 5df74d19189167c244baceee302b8afa2c09c024 (dynamic averaging to incorporate two-stain matches).
1 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for arvindkrishna87/STAT390_SP25_CMIL: Delivered SSIM-guided dynamic averaging for multi-stain image patches. Introduced a dynamic averaging strategy that uses SSIM to decide whether Sox10 stained images should be included in the averaging with H&E and Melan. If SSIM is below threshold, only H&E and Melan are averaged; if SSIM meets threshold, all three (H&E, Melan, Sox10) are averaged. This enhances robustness of image analysis by handling misalignments in stain imaging and improves the reliability of downstream metrics. This work supports more automated, scalable pathology analysis with reduced need for manual corrections. Commits: 5df74d19189167c244baceee302b8afa2c09c024 (dynamic averaging to incorporate two-stain matches).
June 2025
May 2025
6 Commits • 2 Features
May 1, 2025Concise monthly summary for May 2025 focusing on business value and technical achievements for arvindkrishna87/STAT390_SP25_CMIL. Delivered enhancements to cross-stain histology analysis and introduced AlexNet-based classification with robust evaluation. Implemented key fixes to improve stability and cross-stain consistency. Demonstrated end-to-end ML and image-analysis capabilities, from preprocessing and patch extraction to model training and evaluation, with a focus on reproducibility and scalability.
May 2025
6 Commits • 2 Features
May 1, 2025Concise monthly summary for May 2025 focusing on business value and technical achievements for arvindkrishna87/STAT390_SP25_CMIL. Delivered enhancements to cross-stain histology analysis and introduced AlexNet-based classification with robust evaluation. Implemented key fixes to improve stability and cross-stain consistency. Demonstrated end-to-end ML and image-analysis capabilities, from preprocessing and patch extraction to model training and evaluation, with a focus on reproducibility and scalability.
April 2025
3 Commits • 2 Features
Apr 1, 2025April 2025 — End-to-end preprocessing exploration and CNN/AlexNet prototype delivery for arvindkrishna87/STAT390_SP25_CMIL. This month focused on delivering reproducible pipelines and documentation to accelerate experimentation with image data, establishing groundwork for model benchmarking and deployment readiness. Major bugs fixed: none reported.
3 Commits • 2 Features
Apr 1, 2025April 2025 — End-to-end preprocessing exploration and CNN/AlexNet prototype delivery for arvindkrishna87/STAT390_SP25_CMIL. This month focused on delivering reproducible pipelines and documentation to accelerate experimentation with image data, establishing groundwork for model benchmarking and deployment readiness. Major bugs fixed: none reported.
April 2025
Activity
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Quality Metrics
Correctness84.0%
Maintainability80.0%
Architecture77.0%
Performance71.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Computer VisionData AnalysisData AugmentationData PreprocessingData ProcessingDeep LearningImage ClassificationImage ProcessingJupyter NotebookKerasMachine LearningModel EvaluationPyTorchPythonScientific Computing
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