Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms were implemented with three networks (VGG16, ResNet34, and GoogLeNet) and trained using 1000 unannotated US images (500 benign and 500 malignant masses). Two sets of 200 images (100 benign and 100 malignant masses) were used for internal and external validation sets. For comparison with fully-supervised algorithms, ROI annotation was performed manually and automatically. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map, we determined how accurately the weakly-supervised DL algorithms localized the breast masses. For internal validation sets, the weakly-supervised DL algorithms achieved excellent diagnostic performances, with AUC values of 0.92–0.96, which were not statistically different (all Ps > 0.05) from those of fully-supervised DL algorithms with either manual or automated ROI annotation (AUC, 0.92–0.96). For external validation sets, the weakly-supervised DL algorithms achieved AUC values of 0.86–0.90, which were not statistically different (Ps > 0.05) or higher (P = 0.04, VGG16 with automated ROI annotation) from those of fully-supervised DL algorithms (AUC, 0.84–0.92). In internal and external validation sets, weakly-supervised algorithms could localize 100% of malignant masses, except for ResNet34 (98%). The weakly-supervised DL algorithms developed in the present study were feasible for US diagnosis of breast cancer with well-performing localization and differential diagnosis.
A data-oriented approach including all deep learning methods is usually suffered by overfitting. A regularizer has been, from the beginning, introduced to resolve this problem. Inspired by Generative Adversarial Network (GAN), our framework generates the adversarial loss to penalize a segmentation model like a regularizer. We introduce temperature as a regularizer when calculating Least-Square losses. Temperature affects losses in both a discriminator and a generator in our DCGAN framework. Our experiment suggests L2 losses on top of the original LSGAN losses for optimization. This new regularizer using temperature improves semantic Segmentation accuracy both in Pixel accuracy and mean Intersection-of Union.
Related WorksGenerative Adversarial Network: Started from the first GAN paper [6] which introduces the adversarial network design between a generator and a discriminator, various GAN loss functions are introduced such as minimax loss strategy used in DCGAN [7], Least-Square loss from LSGAN [8], or Wasserstein
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