Computer Vision: Buy or DIY?
Four reasons why you might need to build your own computer vision solution.
Continue ReadingFour reasons why you might need to build your own computer vision solution.
Continue ReadingA reading list for anyone looking to adapt their machine learning expertise to computational pathology.
Continue ReadingThe benefits of a self-supervised vision model for generalizability, adaptability, and more.
Continue ReadingMachine learning projects are complex and iterative with uncertain outcomes. These are some ways to improve the efficacy of ML development.
Continue ReadingThe episodes that I highlight below are the 10 most downloaded. The common themes are food and disease: farming, climate risks, food waste, cancer, and medical imaging.
Continue ReadingCommon challenges in machine learning for pathology.
Continue ReadingThe core components to successful machine learning project.
Continue ReadingSome of the key components for robust models: quality control, generalizable, and properly validated.
Continue ReadingNew deep learning technology using H&E images has created an alternative path for molecular diagnostics
Continue ReadingExploring a variety of approaches: stain normalization, color augmentation, adversarial domain adaptation, model adaptation, and finetuning.
Continue ReadingAdvances in AI applying deep learning to digital pathology images can stratify patients by risk.
Continue ReadingA review of machine learning techniques for predicting patient outcomes from whole slide images.
Continue Reading