Ruihan Zhang^1,2, Junhao Yang¹, Chunxiao Chen^1,*

Molecular & Cellular Biomechanics, Vol.15, No.3, pp. 177-187, 2018, DOI: 10.3970/mcb.2018.04292

Abstract The proportion of cells staining for the nuclear antigen Ki-67 is an important predictive indicator for assessment of tumor cell proliferation and growth in routine pathological investigation. Instead of traditional scoring methods based on the experience of a trained laboratory scientist, deep learning approach can be automatically used to analyze the expression of Ki-67 as well. Deep learning based on convolutional neural networks (CNN) for image classification and single shot multibox detector (SSD) for object detection are used to investigate the expression of Ki-67 for assessment of biopsies from patients with breast cancer in this study. The results focus on… More >

Yecai Guo^1,2,*, Chen Li^1,2, Qi Liu³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 829-843, 2019, DOI:10.32604/cmc.2019.03729

Abstract Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems. Hence, it is necessary to address the problem of eliminating rain streaks from the individual rainy image. In this work, a deep convolution neural network (CNN) based method is introduced, called Rain-Removal Net (R²N), to solve the single image de-raining issue. Firstly, we decomposed the rainy image into its high-frequency detail layer and low-frequency base layer. Then, we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its… More >