Accurate breast mass segmentation of automated breast ultrasound (ABUS) images plays a crucial role in 3D breast reconstruction which can assist radiologists in surgery planning. Although the convolutional neural network has great potential for breast mass segmentation due to the remarkable progress of deep learning, the lack of annotated data limits the performance of deep CNNs. In this paper, we present an uncertainty aware temporal ensembling (UATE) model for semi-supervised ABUS mass segmentation. Specifically, a temporal ensembling segmentation (TEs) model is designed to segment breast mass using a few labeled images and a large number of unlabeled images. Considering the network output contains correct predictions and unreliable predictions, equally treating each prediction in pseudo label update and loss calculation may degrade the network performance. To alleviate this problem, the uncertainty map is estimated for each image. Then an adaptive ensembling momentum map and an uncertainty aware unsupervised loss are designed and integrated with TEs model. The effectiveness of the proposed UATE model is mainly verified on an ABUS dataset of 107 patients with 170 volumes, including 8856 2D labeled slices. The Jaccard index (JI), Dice similarity coefficient (DSC), pixel-wise accuracy (AC) and Hausdorff distance (HD) of the proposed method on testing set are 63.65%, 74.25%, 99.21% and 3.81mm respectively. Experimental results demonstrate that our semi-supervised method outperforms the fully supervised method, and get a promising result compared with existing semi-supervised methods.

Early detection of lung cancer is crucial in reducing mortality. Magnetic resonance imaging (MRI) may be a viable imaging technique for lung cancer detection. Numerous lung nodule detection methods have been studied for computed tomography (CT) images. However, to the best of our knowledge, no detection methods have been carried out for the MR images. In this paper, a lung nodule detection method based on deep learning is proposed for thoracic MR images. With parameter optimizing, spatial three-channel input construction, and transfer learning, a faster R-convolution neural network (CNN) is designed to locate the lung nodule region. Then, a false positive (FP) reduction scheme based on anatomical characteristics is designed to reduce FPs and preserve the true nodule. The proposed method is tested on 142 T2-weighted MR scans from the First Affiliated Hospital of Guangzhou Medical University. The sensitivity of the proposed method is 85.2% with 3.47 FPs per scan. The experimental results demonstrate that the designed faster R-CNN network and the FP reduction scheme are effective in the lung nodule detection and the FP reduction for MR images.

Tumor segmentation in 3D automated breast ultrasound (ABUS) plays an important role in breast disease diagnosis and surgical planning. However, automatic segmentation of tumors in 3D ABUS images is still challenging, due to the large tumor shape and size variations, and uncertain tumor locations among patients. In this paper, we develop a novel cross-model attention-guided tumor segmentation network with a hybrid loss for 3D ABUS images. Specifically, we incorporate the tumor location into a segmentation network by combining an improved 3D Mask R-CNN head into V-Net as an end-to-end architecture. Furthermore, we introduce a cross-model attention mechanism that is able to aggregate the segmentation probability map from the improved 3D Mask R-CNN to each feature extraction level in the V-Net. Then, we design a hybrid loss to balance the contribution of each part in the proposed cross-model segmentation network. We conduct extensive experiments on 170 3D ABUS from 107 patients. Experimental results show that our method outperforms other state-of-the-art methods, by achieving the Dice similarity coefficient (DSC) of 64.57%, Jaccard coefficient (JC) of 53.39%, recall (REC) of 64.43%, precision (PRE) of 74.51%, 95th Hausdorff distance (95HD) of 11.91 mm, and average surface distance (ASD) of 4.63 mm. Our code will be available online ( https://github.com/zhouyuegithub/CMVNet ).

Infrared and visible image fusion aims to generate one image with comprehensive information. It can maintain rich texture characteristics and thermal information. However, for existing image fusion methods, the fused images either sacrifice the salience of thermal targets and the richness of textures or introduce the interference of useless information like artifacts. To alleviate these problems, an effective cross-modal coordinate attention network for infrared and visible image fusion called CCAFusion is proposed in this paper. To fully integrate complementary features, the cross-modal image fusion strategy based on coordinate attention is designed, which consists of the feature-awareness fusion module and the feature-enhancement fusion module. Moreover, a multiscale skip connection-based network is employed to obtain multiscale features in the infrared image and the visible image, which can fully utilize the multi-level information in the fusion process. To reduce the discrepancy between the fused image and the input images, a multiple constrained loss function including the base loss and the auxiliary loss is developed to adjust the gray-level distribution and ensure the harmonious coexistence of structure and intensity in fused images, thereby preventing the pollution of useless information like artifacts. Extensive experiments conducted on widely used datasets demonstrate that our CCAFusion achieves superior performance over state-of-the-art image fusion methods in both qualitative evaluation and quantitative measurement. Furthermore, the application to salient object detection reveals the potential of our CCAFusion for high-level vision tasks, which can effectively boost the detection performance.

Unsupervised cross domain (UCD) person re-identification (re-ID) aims to apply a model trained on a labeled source domain to an unlabeled target domain. It faces huge challenges as the identities have no overlap between these two domains. At present, most UCD person re-ID methods perform “supervised learning” by assigning pseudo labels to the target domain, which leads to poor re-ID performance due to the pseudo label noise. To address this problem, a multi-loss optimization learning (MLOL) model is proposed for UCD person re-ID. In addition to using the information of clustering pseudo labels from the perspective of supervised learning, two losses are designed from the view of similarity exploration and adversarial learning to optimize the model. Specifically, in order to alleviate the erroneous guidance brought by the clustering error to the model, a ranking-average-based triplet loss learning and a neighbor-consistency-based loss learning are developed. Combining these losses to optimize the model results in a deep exploration of the intra-domain relation within the target domain. The proposed model is evaluated on three popular person re-ID datasets, Market-1501, DukeMTMC-reID, and MSMT17. Experimental results show that our model outperforms the state-of-the-art UCD re-ID methods with a clear advantage.

Breast cancer has become an important factor affecting human health. Diagnosis based on pathological images is considered the gold standard in the clinic. In this paper, an automatic breast cancer detection method based on hybrid features is proposed for pathological images. To obtain better segmentation results under conditions of crowded and chromatin-sparse nuclei, a 3-output convolutional neural network (CNN) is employed to segment the nuclei. Due to the weak correlation between the hematoxylin (H) and eosin (E) channels, texture features are separately extracted for the two channels, which provides more representative results. From multiple perspectives, the morphological features, spatial structural features and texture features are extracted and fused. Using a support vector machine (SVM) classifier with improved generalization, the pathological image is classified as benign or malignant on the basis of the relief method for feature selection. For the University of California, Santa Barbara database (UCSB), the classification accuracy of the method is 96.7%, and the area under the curve (AUC) is 0.983. The experimental results show that the proposed method yields superior classification performance compared with existing techniques.