Differential Data Augmentation Techniques for Medical Imaging Classification Tasks

By Zeshan Hussain, Francisco Gimenez, Darvin Yi, Daniel L. Rubin. American Medical Informatics Association Annual Symposium, 2017

366 citations recorded by the source corpus.

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Audioclip: Extending Clip to Image, Text and Audio (2021, background): “Random Noise: The addition of random noise was shown to be helpful to overcome overfitting in visual-realted tasks [12].”
Data Augmentation in Classification and Segmentation: A Survey and New Strategies (2023, methodology): “, 2017 [68] Digital Database for Screening Mammography (DDSM).”
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system (2020, unknown): “For brevity, we used the [min, max] interval format in programming for WW and WL. Specifically 5) We applied typical data augmentation techniques [52,53] to increase the diversity of data.”
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation (2019, unknown): “5], similarly to [35, 40].”