Authors: Adrit Rao, Andrea Fisher, Ken Chang, John Christopher Panagides, Katherine McNamara, Joon-Young Lee, Oliver Aalami
Published on: April 17, 2024
Impact Score: 8.2
Arxiv code: Arxiv:2404.10965
Summary
- What is new: Introduction of the Interactive Medical Image Learning (IMIL) framework for clinician-guided data augmentation.
- Why this is important: Loss of clinically relevant information in medical images due to common augmentation techniques, leading to incorrect predictions.
- What the research proposes: IMIL allows for clinician-guided intermediate training data augmentations focusing on relevant visual information, with irrelevant regions ‘blacked out’.
- Results: A 4.2% improvement in accuracy over ResNet-50 using IMIL on only 4% of the training set.
Technical Details
Technological frameworks used: Interactive Medical Image Learning (IMIL)
Models used: ResNet-50
Data used: Radiology imaging data
Potential Impact
Healthcare imaging analysis software providers, radiology departments, and medical AI development companies.
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