When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high-level vision tasks. On the other hand, with the guidance of high-level vision information, the denoising network can generate more visually appealing results. To the best of our knowledge, this is the first work investigating the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning. The code is available online https://github.com/Ding-Liu/DeepDenoising.Comment: the 27th International Joint Conference on Artificial Intelligence (2018

Up-regulation and clinical relevance of novel helicase homologue DHX32 in colorectal cancer

Xiamen Bureau for Science and Technology [A0000033]Background: This study aimed to find novel biomarkers for colorectal cancer. Methods: Fluorescent mRNA differential display PCR (DD-PCR) was used to screen the genes differentially expressed in colorectal cancer tissues and their adjacent tissues. The differentially expressed genes were confirmed by real-time PCR and then their clinical relevance (such as association with tumor location and lymph gland metastasis) was further investigated. Results: We identified by DD-PCR a novel RNA helicase, DHX32, which showed higher expression in colorectal cancer tissues than their adjacent tissues, and this result was confirmed by real time RT-PCR. In addition, we found that the level of DHX32 gene expression in colorectal cancer was significantly associated with cancer location, lymph gland metastasis, cancer nodal status, differentiation grade, and Dukes, stage. Conclusion: DHX32 may play an important role in the development of colorectal cancer and could serve as a novel biomarker for colorectal cancer after additional investigation

Economic Burden for Lung Cancer Survivors in Urban China.

BackgroundWith the rapid increase in the incidence and mortality of lung cancer, a growing number of lung cancer patients and their families are faced with a tremendous economic burden because of the high cost of treatment in China. This study was conducted to estimate the economic burden and patient responsibility of lung cancer patients and the impact of this burden on family income.MethodsThis study uses data from a retrospective questionnaire survey conducted in 10 communities in urban China and includes 195 surviving lung cancer patients diagnosed over the previous five years. The calculation of direct economic burden included both direct medical and direct nonmedical costs. Indirect costs were calculated using the human capital approach, which measures the productivity lost for both patients and family caregivers. The price index was applied for the cost calculation.ResultsThe average economic burden from lung cancer was $43,336 per patient, of which the direct cost per capita was$42,540 (98.16%) and the indirect cost per capita was $795 (1.84$30,277 per capita, which accounted for 171% of the household annual income, a percentage that fell to 107% after subtracting the compensation from medical insurance.ConclusionsThe economic burden for lung cancer patients is substantial in the urban areas of China, and an effective control strategy to lower the cost is urgently needed

AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-based fusion methods can fully exploit the semantic features of each modality, they cannot distinguish the effective low and high frequency information of each modality and fuse them adaptively. To address this issue, we propose AdaFuse, in which multimodal image information is fused adaptively through frequency-guided attention mechanism based on Fourier transform. Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion. The CAF block enhances the high-frequency features of the different modalities so that the details in the fused images can be retained. Moreover, we design a novel loss function composed of structure loss and content loss to preserve both low and high frequency information. Extensive comparison experiments on several datasets demonstrate that the proposed method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics. The ablation experiments also validate the effectiveness of the proposed loss and fusion strategy

Enhanced Nucleate Boiling on Horizontal Hydrophobic-Hydrophilic Carbon Nanotube Coatings

Ideal hydrophobic-hydrophilic composite cavities are highly desired to enhance nucleate boiling. However, it is challenging and costly to fabricate these types of cavities by conventional micro/nano fabrication techniques. In this study, a type of hydrophobic-hydrophilic composite interfaces were synthesized from functionalized multiwall carbon nanotubes by introducing hydrophilic functional groups on the pristine multiwall carbon nanotubes. This type of carbon nanotube enabled hydrophobic-hydrophilic composite interfaces were systematically characterized. Ideal cavities created by the interfaces were experimentally demonstrated to be the primary reason to substantially enhance nucleate boilin

Biphilic Nanoporous Surfaces Enabled Exceptional Drag Reduction and Capillary Evaporation Enhancement

Simultaneously achieving drag reduction and capillary evaporation enhancement is highly desired but challenging because of the trade-off between two distinct hydrophobic and hydrophilic wettabilities. Here, we report a strategy to synthesize nanoscale biphilic surfaces to endow exceptional drag reduction through creating a unique slip boundary condition and fast capillary wetting by inducing nanoscopic hydrophilic areas. The biphilic nanoporous surfaces are synthesized by decorating hydrophilic functional groups on hydrophobic pristine multiwalled carbon nanotubes. We demonstrate that the carbon nanotube-enabled biphilic nanoporous surfaces lead to a 63.1% reduction of the friction coefficient, a 61.7% wetting speed improvement, and up to 158.6% enhancement of capillary evaporation heat transfer coefficient. A peak evaporation heat transfer coefficient of 21.2W/(cm2 K) is achieved on the biphilic surfaces in a vertical direction