no code implementations • 17 Apr 2024 • Adrit Rao, Andrea Fisher, Ken Chang, John Christopher Panagides, Katherine McNamara, Joon-Young Lee, Oliver Aalami
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.
no code implementations • 25 Jan 2024 • Paul Schmiedmayer, Adrit Rao, Philipp Zagar, Vishnu Ravi, Aydin Zahedivash, Arash Fereydooni, Oliver Aalami
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).
no code implementations • 23 Aug 2023 • Adrit Rao, Joon-Young Lee, Oliver Aalami
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.
no code implementations • 2 Sep 2021 • Adrit Rao, Jongchan Park, Sanghyun Woo, Joon-Young Lee, Oliver Aalami
The use of computer vision to automate the classification of medical images is widely studied.
no code implementations • 11 Jul 2018 • Varun Shenoy, Elizabeth Foster, Lauren Aalami, Bakar Majeed, Oliver Aalami
Paired with deep neural networks, they offer the capability to provide clinical insight to assist surgeons during postoperative care.