Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

Though widely used in image classification, convolutional neural networks (CNNs) are prone to noise interruptions, i.e. the CNN output can be drastically changed by small image noise. To improve the noise robustness, we try to integrate CNNs with wavelet by replacing the common down-sampling (max-pooling, strided-convolution, and average pooling) with discrete wavelet transform (DWT). We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet). During the down-sampling, WaveCNets apply DWT to decompose the feature maps into the low-frequency and high-frequency components. Containing the main information including the basic object structures, the low-frequency component is transmitted into the following layers to generate robust high-level features. The high-frequency components are dropped to remove most of the data noises. The experimental results show that WaveCNets achieve higher accuracy on ImageNet than various vanilla CNNs. We have also tested the performance of WaveCNets on the noisy version of ImageNet, ImageNet-C and six adversarial attacks, the results suggest that the proposed DWT/IDWT layers could provide better noise-robustness and adversarial robustness. When applying WaveCNets as backbones, the performance of object detectors (i.e., faster R-CNN and RetinaNet) on COCO detection dataset are consistently improved. We believe that suppression of aliasing effect, i.e. separation of low frequency and high frequency information, is the main advantages of our approach. The code of our DWT/IDWT layer and different WaveCNets are available at https://github.com/CVI-SZU/WaveCNet.

Depression is a serious mental disorder affecting millions of people. Traditional clinical diagnosis methods are subjective, complicated and require extensive participation of clinicians. Recent advances in automatic depression analysis systems promise a future where these shortcomings are addressed by objective, repeatable, and readily available diagnostic tools to aid health professionals in their work. Yet there remain a number of barriers to the development of such tools. One barrier is that existing automatic depression analysis algorithms base their predictions on very brief sequential segments, sometimes as little as one frame. Another barrier is that existing methods do not take into account what the context of the measured behaviour is. In this paper, we extract multi-scale video-level features for video-based automatic depression analysis. We propose to use automatically detected human behaviour primitives as the low-dimensional descriptor for each frame. We also propose two novel spectral representations to represent video-level multi-scale temporal dynamics of expressive behaviour. Constructed spectral representations are fed to CNNs and ANNs for depression analysis. In addition to achieving state-of-the-art accuracy in depression severity estimation, we show that the task conducted by the user matters, that fusion of a combination of tasks reaches highest accuracy, and that longer tasks are more informative than shorter tasks, up to a point.

Owing to the fast developments of economics, a lot of devices and objects have been connected and have formed the Internet of Things (IoT). Visual sensors have been applied in vehicle navigation, traffic situational awareness, and traffic safety management. However, the particles in the air degrade the imaging quality, which affects the performance of vehicle navigation, traffic situational awareness, and traffic safety management. Deep-learning-based dehazing methods were proposed to address this issue. However, these methods are trained with simulated hazy images and cannot generalize to natural haze images well. To address the domain shift problem, some methods resort to zero-shot learning or domain adaption to boost the generalization of the model on natural haze images. However, the relevance between dehazed results and clean images is ignored by zero-shot dehazing methods. Domain-adaption-based dehazing methods ignore the relationship between the dehazed results and the hazy images. To overcome these issues, a generative adversarial and self-supervised dehazing network is introduced to boost the dehazing performance on real haze images. First, generative adversarial is employed to construct the relevance between dehazed results and haze-free images, which can boost the natural appearance of dehazed results. Second, self-supervised learning is employed to construct the relevance between the dehazed results and hazy images, which can restrict the solution space of dehazing. To show the effectiveness of the proposed model, we conduct extensive experiments on real and simulated haze images. Compared with state-of-the-art methods, the proposed model achieves state-of-the-art dehazing performance.