Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

Ravi K. Samala; Heang-Ping Chan; Lubomir Hadjiiski; Mark A. Helvie; Caleb Richter; Kenny Cha

doi:10.1117/12.2293412

27 February 2018 Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, Caleb Richter, Kenny Cha

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105750Q (2018) https://doi.org/10.1117/12.2293412
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78±0.02 and 0.82±0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90±0.04 on the independent DBT test set.

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Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, Caleb Richter, and Kenny Cha "Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750Q (27 February 2018); https://doi.org/10.1117/12.2293412

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