Fine-grained classification and grading of breast cancer (BC) histopathological images are of great value in clinical application. However, automatic classification and grading of BC histopathological images are complicated by (1) small inter-class variance and large intraclass variance exist in BC histopathological images, and (2) features extracted from similar histopathological images with different magnification are quite different. To address these issues, an improved deep convolution neural network model is proposed and the procedure can be divided into three main stages. Firstly, in the representation learning process, multiclass recognition task and verification task of image pair are combined. Secondly, in the feature extraction process, a prior knowledge is built, which is "the variances in feature outputs between different subclasses is relatively large while the variance between the same subclass is small." Additionally, the prior information that histopathological images with different magnification belong to the same subclass are embedded in the feature extraction process, which contributes to less sensitive with image magnification. The experimental results based on three different histopathological image datasets show that the performance of the proposed method is better than state of the art, with better robustness and generalization ability. Keywords Multi-task deep learning • Histopathological image classification • Fine-grained • Convolutional neural network • Breast cancer Lingqiao Li and Xipeng Pan are contributed equally
Multispectral pedestrian detection, which consists of a color stream and thermal stream, is essential under conditions of insufficient illumination because the fusion of the two streams can provide complementary information for detecting pedestrians based on deep convolutional neural networks (CNNs). In this paper, we introduced and adapted a simple and efficient one-stage YOLOv4 to replace the current state-of-the-art two-stage fast-RCNN for multispectral pedestrian detection and to directly predict bounding boxes with confidence scores. To further improve the detection performance, we analyzed the existing multispectral fusion methods and proposed a novel multispectral channel feature fusion (MCFF) module for integrating the features from the color and thermal streams according to the illumination conditions. Moreover, several fusion architectures, such as Early Fusion, Halfway Fusion, Late Fusion, and Direct Fusion, were carefully designed based on the MCFF to transfer the feature information from the bottom to the top at different stages. Finally, the experimental results on the KAIST and Utokyo pedestrian benchmarks showed that Halfway Fusion was used to obtain the best performance of all architectures and the MCFF could adapt fused features in the two modalities. The log-average miss rate (MR) for the two modalities with reasonable settings were 4.91% and 23.14%, respectively.
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