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Chest X‐ray images taken under different conditions follow different distributions, preventing the models trained on a domain from generalising well on the other domain. In this paper, a generative domain adaptation (GDA) method is proposed to address this issue and facilitate the learning process for downstream analysis. GDA adapts different domains to a virtual common one where images are aligned at the appearance level. To this end, a domain shared generator is used to transform the input images and two competitive discriminators are used to adversarially supervise the transforming process. The domain discriminator drives the generator to narrow the domain gap while the fidelity discriminator forces the generator to keep the inherent information. Moreover, a specific classification or detection network is attached to the generator to supervise it in a task‐oriented manner. Experiment results on a large‐scale dataset containing 46k chest X‐ray images demonstrate that GDA outperforms representative domain adaptation methods by a large margin for both disease classification and lesion detection as well as provides useful transformed images to assist experts for diagnosis. [ABSTRACT FROM AUTHOR]

Deep learning occupies an undisputed dominance in crowd counting. This paper proposes a novel convolutional neural network architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposed SegCrowdNet still adaptively highlights the human head region and suppresses the non‐head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi‐scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of this proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that the proposed SegCrowdNet achieves excellent performance compared with the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Over the past two years deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this study, the authors present a novel semantic segmentation method of using a deep fully convolutional neural network to achieve image segmentation results with more precise boundary localisation. The above segmentation engine is trainable, and consists of an encoder network with widening residual skipped connections and a decoder network with a pixel‐wise classification layer. Here the encoder network with widening residual skipped connections allows the combination of shallow layer features and deep layer semantic features, and the decoder network with classification layer maps the low‐resolution encoder features to full resolution image with pixel‐wise classification. The experimental results on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset show that the proposed method is effective and competitive. [ABSTRACT FROM AUTHOR]

In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR‐based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub‐dictionaries are learned from numerous high‐quality image patches which have been pre‐classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub‐dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi‐focus and multi‐modal image sets demonstrate that the ASR‐based fusion method can outperform the conventional SR‐based method in terms of both visual quality and objective assessment. [ABSTRACT FROM AUTHOR]