Jaewon Jung

Generative adversarial networks (GANs) have demonstrated remarkable performance in various fashion-related applications, including virtual try-ons, compatible clothing recommendations, fashion editing, and the generation of fashion items. Despite this progress, limited research has addressed the specific challenge of generating a compatible fashion item with an ensemble consisting of distinct categories, such as tops, bottoms, and shoes. In response to this gap, we propose a novel GANs framework, termed Deep Fashion Designer Generative Adversarial Networks (DFDGAN), designed to address this challenge. Our model accepts a series of source images representing different fashion categories as inputs and generates a compatible fashion item, potentially from a different category. The architecture of our model comprises several key components: an encoder, a mapping network, a generator, and a discriminator. Through rigorous experimentation, we benchmark our model against existing baselines, validating the effectiveness of each architectural choice. Furthermore, qualitative results indicate that our framework successfully generates fashion items compatible with the input items, thereby advancing the field of fashion item generation.

Mammography plays an important role in screening breast cancer among females, and artificial intelligence has enabled the automated detection of diseases on medical images. This study aimed to develop a deep learning model detecting breast cancer in digital mammograms of various densities and to evaluate the model performance compared to previous studies. From 1501 subjects who underwent digital mammography between February 2007 and May 2015, craniocaudal and mediolateral view mammograms were included and concatenated for each breast, ultimately producing 3002 merged images. Two convolutional neural networks were trained to detect any malignant lesion on the merged images. The performances were tested using 301 merged images from 284 subjects and compared to a meta-analysis including 12 previous deep learning studies. The mean area under the receiver-operating characteristic curve (AUC) for detecting breast cancer in each merged mammogram was 0.952 ± 0.005 by DenseNet-169 and 0.954 ± 0.020 by EfficientNet-B5, respectively. The performance for malignancy detection decreased as breast density increased (density A, mean AUC = 0.984 vs. density D, mean AUC = 0.902 by DenseNet-169). When patients’ age was used as a covariate for malignancy detection, the performance showed little change (mean AUC, 0.953 ± 0.005). The mean sensitivity and specificity of the DenseNet-169 (87 and 88%, respectively) surpassed the mean values (81 and 82%, respectively) obtained in a meta-analysis. Deep learning would work efficiently in screening breast cancer in digital mammograms of various densities, which could be maximized in breasts with lower parenchyma density.