Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.

InChan Hwang,Hari Trivedi,Beatrice Brown-Mulry,Linglin Zhang,Vineela Nalla,Aimilia Gastounioti,Judy Gichoya,Laleh Seyyed-Kalantari,Imon Banerjee,MinJae Woo

Frontiers in radiology（2023）

引用 0|浏览18

暂无评分

摘要

The degradation may potentially be due to ( 1) a mismatch in features between film-based and digital mammograms ( 2) a mismatch in pathologic and radiological information. In conclusion, use of both film and digital mammography during training may hinder modern models designed for breast cancer screening. Caution is required when combining film-based and digital mammograms or when utilizing pathologic and radiological information simultaneously.

查看译文

关键词

mammography,convolutional neural networks,abnormality classification,neural networks,multi-source

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要