Lightning Talk: A Novel Domain Generalization Technique for Medical Imaging Using... - Dinkar Juyal
Lightning Talk: A Novel Domain Generalization Technique for Medical Imaging Using PyTorch - Dinkar Juyal, PathAI
Domain generalization is critical for real-world applications of machine learning models to medical imaging. Variation in histopathology images arises through a complex combination of factors relating to tissue collection and laboratory processing, as well as factors intrinsic to patient samples. Therefore, augmentation-based methods of domain generalization that require domain identifiers and manual fine-tuning are inadequate in this setting. To overcome this challenge, we introduce ContriMix, a domain generalization technique that learns to generate synthetic images by disentangling and permuting the biological content ("content") and technical variations ("attributes") in images. ContriMix does not rely on domain identifiers or handcrafted augmentations and makes no assumptions about the input characteristics of images. ContriMix produces SOTA results on Camelyon17 dataset in Stanford WILDS public leaderboard. ContriMix is developed entirely in PyTorch. The modular nature of PyTorch enables the use of ContriMix as an easy and intuitive plug-and-play setup to generate realistic synthetic medical images at the time of model training. Inference code is available.
