Results 11 to 20 of about 2,410 (213)
Magnification Generalization For Histopathology Image Embedding [PDF]
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021 Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.
Milad Sikaroudi +4 more
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Gigapixel Histopathological Image Analysis Using Attention-Based Neural Networks
IEEE Access, 2021 Although CNNs are widely considered as the state-of-the-art models in various applications of image analysis, one of the main challenges still open is the training of a CNN on high resolution images.
Nadia Brancati +3 more
doaj +1 more source
Histopathological Image Analysis: A Review [PDF]
IEEE Reviews in Biomedical Engineering, 2009 Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form ...
Gurcan, Metin N. +5 more
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BMC Medical Informatics and Decision Making, 2022 Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye ...
Xiaogang Dong +8 more
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Artifact Removal in Histopathology Images
CoRR, 2022 Corrected typos, small modification of Figure 1 (+ reflected in Section 2.1), results ...
Cameron Dahan +3 more
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Difficulty Translation in Histopathology Images [PDF]
, 2020 The unique nature of histopathology images opens the door to domain-specific formulations of image translation models. We propose a difficulty translation model that modifies colorectal histopathology images to be more challenging to classify. Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an ...
Jerry W. Wei +7 more
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Progress of Machine Vision in the Detection of Cancer Cells in Histopathology
IEEE Access, 2022 In recent years, with the rapid development of artificial intelligence, machine vision technology has been widely used in various fields. Traditional cancer detection methods are time-consuming, labor-intensive, and highly dependent on the experience of ...
Wenbin He +10 more
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Entropy, 2021 Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images.
Zakaria Senousy +3 more
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Optoacoustic imaging of the breast: correlation with histopathology and histopathologic biomarkers [PDF]
European Radiology, 2019 This study was conducted in order to investigate the role of gray-scale ultrasound (US) and optoacoustic imaging combined with gray-scale ultrasound (OA/US) to better differentiate between breast cancer molecular subtypes.All 67 malignant masses included in the Maestro trial were retrospectively reviewed to compare US and OA/US feature scores and ...
Menezes, G.L.G. +7 more
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Ontology-Driven Image Analysis for Histopathological Images [PDF]
, 2010 Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images.
Othmani, Ahlem +2 more
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