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An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging
In this paper, we investigate the deleterious impact of a taxonomy of data heterogeneity regimes on federated learning methods, including quantity skew, label distribution skew, and imaging acquisition skew.
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging
Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients.
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
Our controlled experiments show that the keys to improving downstream performance on disease classification are (1) using patient metadata to appropriately create positive pairs from different images with the same underlying pathologies, and (2) maximizing the number of different images used in query pairing.
Prediction of Small Molecule Kinase Inhibitors for Chemotherapy Using Deep Learning
The current state of cancer therapeutics has been moving away from one-size-fits-all cytotoxic chemotherapy, and towards a more individualized and specific approach involving the targeting of each tumor's genetic vulnerabilities.
Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learning
We train and evaluate our multi-agent methods against a team operating with a smart hand-coded policy.
Institutionally Distributed Deep Learning Networks
In such cases, sharing a deep learning model is a more attractive alternative.
