
Developed a Multiple Instance Learning (MIL) workflow for histology image analysis in the arvindkrishna87/STAT390_SP25_CMIL repository, focusing on H&E stained samples. Leveraged PyTorch and Python to implement a MIL-based patch analysis pipeline, integrating a DenseNet backbone and addressing class imbalance challenges. Enhanced project maintainability through comprehensive folder reorganization, improved documentation, and structured README updates. Experimented with data preprocessing techniques, including padding-based transforms to preserve image context. Supported model evaluation and stakeholder communication by preparing detailed presentation materials and refining code in Jupyter Notebooks. The work emphasized code organization, deep learning, and reproducible research practices over a focused two-month period.
Concise monthly summary for May 2025 focusing on business value and technical achievements for arvindkrishna87/STAT390_SP25_CMIL. The month included major repository and workflow improvements, MIL experimentation with a pretrained DenseNet backbone, and comprehensive updates to documentation and presentation materials to support stakeholder communication and future model evaluation.
Concise monthly summary for May 2025 focusing on business value and technical achievements for arvindkrishna87/STAT390_SP25_CMIL. The month included major repository and workflow improvements, MIL experimentation with a pretrained DenseNet backbone, and comprehensive updates to documentation and presentation materials to support stakeholder communication and future model evaluation.
April 2025 performance snapshot for repository arvindkrishna87/STAT390_SP25_CMIL. Focused on implementing a MIL-based patch analysis workflow for histology (H&E) images and validating its training pipeline.
April 2025 performance snapshot for repository arvindkrishna87/STAT390_SP25_CMIL. Focused on implementing a MIL-based patch analysis workflow for histology (H&E) images and validating its training pipeline.

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