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Kernel representation-based End-to-End network-enabled decoding strategy for precise and medical diagnosis.
J Hazard Mater
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430070, China. Electronic address:
Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomarker identification, the diversity in the density and adherence of targets still poses a serious challenge. In this regard, we propose CellNet, a neural network model specifically designed for detecting dense targets.
View Article and Find Full Text PDFIn this Letter, we propose a high-performance optimized detection scheme based on a neural network (NN) in a receiver digital signal processing (DSP) for bandwidth-limited intensity modulation and direct detection (IM/DD) transmission systems. The NN-based optimized detection scheme consists of two components, an NN-based lookup table (NN-LUT) and an NN-based log-maximum estimation with a fixed number of surviving state (NN-MAP) decoder. The NN-LUT provides more accurate and sufficient information (PI) to the decoder than the conventional filter-form PI without increasing computational complexity.
View Article and Find Full Text PDFA mutual inclusion mechanism for precise boundary segmentation in medical images.
Front Bioeng Biotechnol
December 2024
School of Information Engineering, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
Introduction: Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings.
View Article and Find Full Text PDFHistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.
BMC Med Imaging
January 2025
Department of Information, Third Affiliated Hospital of Naval Medical University, No. 225 Changhai Road, Yangpu District, 200438, Shanghai, China.
Purpose: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital pathology workflow.
Methods: We propose a dual-mechanism feature pyramid fusion technique that integrates nuclear segmentation and classification tasks to construct the HistoNeXt network model. HistoNeXt utilizes an encoder-decoder architecture, where the encoder, based on the advanced ConvNeXt convolutional framework, efficiently and accurately extracts multi-level abstract features from tissue images.
State-of-the-Art Trends in Data Compression: COMPROMISE Case Study.
Entropy (Basel)
November 2024
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia.
After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight.
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