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Application of Image Segmentation Technology Based on Machine Learning in Medical Image Analysis [PDF]
ITM Web of ConferencesMedical image analysis heavily relies on the crucial step of image segmentation, which possesses the capability to discern and differentiate various structures within medical imagery.
Zhang Yurun
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Transforming the Interactive Segmentation for Medical Imaging
, 2022 Accepted to MICCAI ...
Liu, Wentao +4 more
openaire +2 more sources
Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition
Sensors, 2023 This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast
Deawon Kwak, Jiwoo Choi, Sungjin Lee
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Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
, 2017 We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention
Cobzas, Dana +4 more
core +1 more source
FEBS Letters, EarlyView.Enteropathogenic E. coli (EPEC) infects the human intestinal epithelium, resulting in severe illness and diarrhoea. In this study, we compared the infection of cancer‐derived cell lines with human organoid‐derived models of the small intestine. We observed a delayed in attachment, inflammation and cell death on primary cells, indicating that host ...
Mastura Neyazi +5 more
wiley +1 more source
A review: Deep learning for medical image segmentation using multi-modality fusion
Array, 2019 Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve the segmentation.
Tongxue Zhou, Su Ruan, Stéphane Canu
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Sensors, 2022 The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods
Shaolong Chen +3 more
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Rad27/FEN1 prevents accumulation of Okazaki fragments and ribosomal DNA copy number changes
FEBS Letters, EarlyView.The budding yeast Rad27 is a structure‐specific endonuclease. Here, the authors reveal that Rad27 is crucial for maintaining the stability of the ribosomal RNA gene (rDNA) region. Rad27 deficiency leads to the accumulation of Okazaki fragments and changes in rDNA copy number.
Tsugumi Yamaji +3 more
wiley +1 more source