Melanoma is the most harmful form of skin cancer and they emerge from melanocytes, a type of pigment-producing cells. Melanomas usually occur on the skin;they mostly form around the back for men, whereas the common pl...
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Image and video forgery using cutting-edge deep learning techniques has become one of the major issues in the social networking era. Media manipulation in which one person's face is swapped out for another's o...
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In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional...
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The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have a...
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Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of au...
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In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, signific...
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Image inpainting is a domain in which researchers have shown considerable interest, and when it comes to deep learning techniques, realistic problems become interesting and challenging. In image inpainting, a corrupte...
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Transformers have recently lead to encouraging progress in computer *** this work,we present new baselines by improving the original Pyramid vision Transformer(PVT v1)by adding three designs:(i)a linear complexity att...
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Transformers have recently lead to encouraging progress in computer *** this work,we present new baselines by improving the original Pyramid vision Transformer(PVT v1)by adding three designs:(i)a linear complexity attention layer,(ii)an overlapping patch embedding,and(iii)a convolutional feed-forward *** these modifications,PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification,detection,and *** particular,PVT v2 achieves comparable or better performance than recent work such as the Swin *** hope this work will facilitate state-ofthe-art transformer research in computer *** is available at https://***/whai362/PVT.
Colonoscopy plays a pivotal role in detecting and diagnosing colorectal diseases, with polyp segmentation being a critical step for accurate diagnosis. In this study, we propose a novel approach for polyp segmentation...
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ISBN:
(数字)9798350385922
ISBN:
(纸本)9798350385939
Colonoscopy plays a pivotal role in detecting and diagnosing colorectal diseases, with polyp segmentation being a critical step for accurate diagnosis. In this study, we propose a novel approach for polyp segmentation in colonoscopy images, leveraging the Shuffle attention mechanism within the proposed architecture. Our method is rigorously evaluated across three diverse colonoscopy datasets, demonstrating promising results with an mean dice score of 0.93. Furthermore, to enhance segmentation accuracy, we employ a Conditional Random Field (CRF) post-processing method to refine the segmentation results. Through extensive experimentation and analysis, we showcase the effectiveness of our approach in achieving highly accurate polyp segmentation, thereby contributing to improved diagnostic outcomes in colorectal healthcare. Our method holds significant potential for enhancing computer-aided detection systems in clinical practice, facilitating early detection and treatment of colorectal abnormalities.
Next-generation sequencing is producing data at an exponential rate, which creates immense computation hurdles for reliably and efficiently storing, transmitting, and analysing the data. Compression plays a vital role...
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ISBN:
(数字)9798350383409
ISBN:
(纸本)9798350383416
Next-generation sequencing is producing data at an exponential rate, which creates immense computation hurdles for reliably and efficiently storing, transmitting, and analysing the data. Compression plays a vital role in managing the challenges associated with the growing volume of genomic data. Direct analysis over the compressed representations of DNA sequences can facilitate scalability, enabling the analysis of larger datasets within reasonable computational resources. The proposed scheme develops a methodology to extract meaningful features from compressed DNA sequences that maintain a high correlation with features from the original sequences. We employed deep learning techniques to maximize feature correlation between original and compressed sequence representations. This approach ensures that the features derived from compressed sequences are as informative and representative as those from the original sequences. We evaluated the proposed method with various state-of-the-art approaches for genomic sequence analysis using a dataset that includes genome sequences of different viruses. According to the results, the features extracted from compressed sequences are discriminative for the classification task; thus, downstream analysis of genomic sequences is possible without decompression.
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