Millimeter-wave vector modulator and its application in active phased array antenna

Han, Kewu; Yang, Menglong; Sun, Yun; Li, Lingyun; Hou, Yang; Sun, Xiaowei

doi:10.3724/sp.j.1010.2011.00425

Cited by 1 publication

(1 citation statement)

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“…Computer-aided diagnosis (CAD) integrates imaging technology with medical image processing. It commonly assists pathologists in identifying lesion areas and conducting quantitative analyses to enhance diagnostic accuracy [11]. Recently, Deep Learning (DL) and its associated image-processing techniques have garnered significant attention across numerous scientific fields due to their exceptional performance, particularly in medical imaging applications [12].…”

Section: Intelligent Recognition and Analysis Of Fluorescent Imagementioning

confidence: 99%

A framework for immunofluorescence image augmentation and classification based on unsupervised attention mechanism

Wang

Zhang

Wang

et al. 2023

Journal of Biophotonics

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Autoimmune encephalitis (AE) is a common neurological disorder. As a standard method for neuroautoantibody detection, pathologists use tissue matrix assays (TBA) for initial disease screening. In this study, microscopic fluorescence imaging was combined with deep learning to improve AE diagnostic accuracy. Due to the inter‐class imbalance of medical data, we propose an innovative generative adversarial network supplemented with attention mechanisms to highlight key regions in images to synthesize high‐quality fluorescence images. However, securing annotated medical data is both time‐consuming and costly. To circumvent this problem, we employ a self‐supervised learning approach that utilizes unlabeled fluorescence data to support downstream classification tasks. To better understand the fluorescence properties in the data, we introduce a multichannel input convolutional neural network that adds additional channels of fluorescence intensity. This study builds an AE immunofluorescence dataset and obtains the classification accuracy of 88.5% using our method, thus confirming the effectiveness of the proposed method.This article is protected by copyright. All rights reserved.

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Section: Intelligent Recognition and Analysis Of Fluorescent Imagementioning

confidence: 99%

A framework for immunofluorescence image augmentation and classification based on unsupervised attention mechanism

Wang

Zhang

Wang

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

Millimeter-wave vector modulator and its application in active phased array antenna

Cited by 1 publication

References 1 publication

A framework for immunofluorescence image augmentation and classification based on unsupervised attention mechanism

A framework for immunofluorescence image augmentation and classification based on unsupervised attention mechanism

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