Name
Abstract | ||
|---|---|---|
Mammography is the primary modality for breast cancer screening, attempting to reduce breast cancer mortality risk with early detection. However, robust screening less hampered by misdiagnoses remains a challenge. Deep Learning methods have shown strong applicability to various medical image datasets, primarily thanks to their powerful feature learning capability. Such successful applications are, however, often overshadowed with limitations in real medical settings, dependency of lesion annotations, and discrepancy of data types between training and other datasets. To address such critical challenges, we developed DiaGRAM (Deep GeneRAtive Multi-task), which is built upon the combination of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The enhanced feature learning with GAN, and its incorporation with the hybrid training with the region of interest (ROI) and the whole images results in higher classification performance and an effective end-to-end scheme. DiaGRAM is capable of robust prediction, even for a small dataset, without lesion annotation, via transfer learning capacity. DiaGRAM achieves an AUC of 88.4% for DDSM and even 92.5% for the challenging INbreast with its small data size. |
Year | Venue | Field |
|---|---|---|
2018 | MICCAI | Mammography,Pattern recognition,Convolutional neural network,Computer science,Transfer of learning,Breast cancer screening,Data type,Artificial intelligence,Deep learning,Region of interest,Feature learning |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shayan Shams | 1 | 1 | 0.70 |
| Richard Platania | 2 | 10 | 3.26 |
| Jian Zhang | 3 | 47 | 3.70 |
| Joohyun Kim | 4 | 292 | 22.75 |
| Seung-Jong Park | 5 | 319 | 31.12 |