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Computer Vision: Buy or DIY?
Four reasons why you might need to build your own computer vision solution.
Continue ReadingVision Foundation Models: When Does Size Matter?
Large vision models may seem attractive, but domain-specific models can get you farther.
Continue ReadingGetting Started with Computer Vision for Histopathology
A reading list for anyone looking to adapt their machine learning expertise to computational pathology.
Continue ReadingUnlock the Potential of Your Proprietary Images with a Domain-Specific Foundation Model
The benefits of a self-supervised vision model for generalizability, adaptability, and more.
Continue ReadingHow to Reduce the Trial-and-Error of Machine Learning Development
Machine learning projects are complex and iterative with uncertain outcomes. These are some ways to improve the efficacy of ML development.
Continue ReadingImpact AI: 10 Most Downloaded Episodes
The 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 ReadingPathology AI Pitfalls: Top 10 Challenges in Applying Computer Vision to Histopathology
Common challenges in machine learning for pathology.
Continue ReadingHow to Design a Roadmap for a Machine Learning Project
The core components to successful machine learning project.
Continue ReadingBuilding Robust AI Solutions for Whole Slide Images
Some of the key components for robust models: quality control, generalizable, and properly validated.
Continue ReadingPredicting Molecular Tumor Biomarkers from H&E: A Review of the State-of-the-Art
New deep learning technology using H&E images has created an alternative path for molecular diagnostics
Continue Reading5 Ways to Make Histopathology Image Models More Robust to Domain Shifts
Exploring a variety of approaches: stain normalization, color augmentation, adversarial domain adaptation, model adaptation, and finetuning.
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