Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning
BVM 2022 - German Workshop on Medical Image Computing, Heidelberg / 2022 / workshop
Panambur AB, Madhu P, Maier A
workshop / 2022
Medical AI visual summary
Scientific summary
This BVM study evaluates random histogram equalization as an augmentation strategy for deep-learning-based breast calcification analysis.
Abstract
This BVM 2022 paper evaluates random histogram equalization for deep-learning-based breast calcification analysis.
Why it matters
Calcifications are subtle mammographic findings, making robustness to acquisition and intensity variation clinically relevant.
Contribution
It introduces an image-enhancement augmentation question for mammography calcification classification.
Method overview
Random histogram equalization is applied during model training and compared against baseline deep learning classification.
Key findings
- Published in the BVM 2022 medical image computing proceedings.
- TODO: Add exact metrics from the paper.
Citation
Panambur AB, Madhu P, Maier A. (2022). Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning. BVM 2022 - German Workshop on Medical Image Computing, Heidelberg.
TODO: Add BibTeX.
