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IMIL: Interactive Medical Image Learning Framework
We propose the Interactive Medical Image Learning (IMIL) framework, a novel approach for improving the training of medical image analysis algorithms that enables clinician-guided intermediate training data augmentations on misprediction outliers, focusing the algorithm on relevant visual information.
LLM on FHIR -- Demystifying Health Records
Objective: To enhance health literacy and accessibility of health information for a diverse patient population by developing a patient-centered artificial intelligence (AI) solution using large language models (LLMs) and Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (APIs).
Studying the Impact of Augmentations on Medical Confidence Calibration
In this paper, we evaluate the effects of three modern augmentation techniques, CutMix, MixUp, and CutOut on the calibration and performance of CNNs for medical tasks.
Studying the Effects of Self-Attention for Medical Image Analysis
The use of computer vision to automate the classification of medical images is widely studied.
