Outline

Implement segmentation techniques for nuclei, cells, and regions using methods like thresholding, watershed, StarDist, and Hover-Net. • Extract diverse features including morphology, intensity (DAB OD), texture (GLCM), and spatial correlations (Ripley's K). • Navigate QuPath for ROI definition and deconvolution, and CellProfiler for nuclei feature extraction.

Outline

Prepare data effectively, managing patient-level splits, leakage, stratification, and class imbalance. • Utilize tabular ML models (LR/RF/XGB), CNNs on tiles, and Multiple Instance Learning (MIL) for WSIs. • Develop automated scoring systems for H-score, Allred, and PD-L1 CPS/TPS.

Outline

Evaluate models using patient-level metrics, calibration, and error analysis. • Address cross-site and stain variability and implement domain adaptation strategies. • Conduct hands-on fine-tuning of CNNs, simple MIL implementations, and performance comparison of classical vs. deep models.

Outline

Perform statistical tests including group comparisons, covariate correlations, and survival analysis (KM/Cox). • Interpret ROC/PR curves, thresholds, and decision curves for model assessment. • Generate visual explanations using heatmaps, attention maps, Grad-CAM, and spatial positivity maps.

Outline

Prepare comprehensive reports for clinical translation, including notebooks, figures, and tables. • Understand integration with LIS/PACS and compliance with CAP/CLIA regulations. • Implement deployment strategies for packaging, batch vs. real-time processing, monitoring, and bias/fairness considerations.