
Hannah Zhao developed a Multiple Instance Learning (MIL) workflow for histology image analysis in the arvindkrishna87/STAT390_SP25_CMIL repository, focusing on H&E-stained samples. She implemented a MIL-based patch analysis pipeline using PyTorch and Python, integrating a DenseNet backbone to address class imbalance in medical image datasets. Her work included designing data loading with padding-based transforms, refining model architecture, and improving project structure for maintainability. Hannah also enhanced documentation and presentation materials, supporting reproducibility and stakeholder communication. The depth of her contributions is reflected in the end-to-end workflow, from data preprocessing to model experimentation and comprehensive project organization.
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.

Overview of all repositories you've contributed to across your timeline