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Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI
PurposeA classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL)...
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Expansive Receptive Field and Local Feature Extraction Network: Advancing Multiscale Feature Fusion for Breast Fibroadenoma Segmentation in Sonography
Fibroadenoma is a common benign breast disease that affects women of all ages. Early diagnosis can greatly improve the treatment outcomes and reduce...
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The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations
Fully supervised medical image segmentation methods use pixel-level labels to achieve good results, but obtaining such large-scale, high-quality...
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A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images
Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the...
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SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules
BackgroundMedical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are...
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Invariant Content Representation for Generalizable Medical Image Segmentation
Domain generalization (DG) for medical image segmentation due to privacy preservation prefers learning from a single-source domain and expects good...
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The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading
Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to...
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MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network
Histological assessment of skeletal muscle slices is very important for the accurate evaluation of weightless muscle atrophy. The accurate...
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Residual Deformable Split Channel and Spatial U-Net for Automated Liver and Liver Tumour Segmentation
Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net...
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Background removal for debiasing computer-aided cytological diagnosis
To address the background-bias problem in computer-aided cytology caused by microscopic slide deterioration, this article proposes a deep learning...
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Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data
PurposeThe acquisition conditions of medical imaging are often precisely defined, leading to a high homogeneity among different data sets....
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Unsupervised domain adaptive tumor region recognition for Ki67 automated assisted quantification
PurposeKi67 is a protein associated with tumor proliferation and metastasis in breast cancer and acts as an essential prognostic factor. Clinical...
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Polyp Segmentation Using a Hybrid Vision Transformer and a Hybrid Loss Function
Accurate and early detection of precursor adenomatous polyps and their removal at the early stage can significantly decrease the mortality rate and...
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Deep learning to assess microsatellite instability directly from histopathological whole slide images in endometrial cancer
Molecular classification, particularly microsatellite instability-high (MSI-H), has gained attention for immunotherapy in endometrial cancer (EC)....
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Jigsaw training-based background reverse attention transformer network for guidewire segmentation
PurposeGuidewire segmentation plays a crucial role in percutaneous coronary intervention. However, it is a challenging task due to the low...
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DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation
Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. To overcome the small size...
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Annotation-efficient training of medical image segmentation network based on scribble guidance in difficult areas
PurposeThe training of deep medical image segmentation networks usually requires a large amount of human-annotated data. To alleviate the burden of...
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Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies
Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients...
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Electron Microscopic Map** of Mitochondrial Morphology in the Cochlear Nerve Fibers
To enable nervous system function, neurons are powered in a use-dependent manner by mitochondria undergoing morphological-functional adaptation. In a...
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Glomerulus Detection Using Segmentation Neural Networks
Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in...
