Results 11 to 20 of about 256,649 (274)
Machine learning methods for histopathological image analysis
Computational and Structural Biotechnology Journal, 2017 Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques.
Ishikawa, Shumpei, Komura, Daisuke
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Neural Image Compression for Gigapixel Histopathology Image Analysis [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 We propose Neural Image Compression (NIC), a two-step method to build convolutional neural networks for gigapixel image analysis solely using weak image-level labels. First, gigapixel images are compressed using a neural network trained in an unsupervised fashion, retaining high-level information while suppressing pixel-level noise.
Tellez, D. +5 more
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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.
Sikaroudi, Milad +4 more
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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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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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Pan-cancer classifications of tumor histological images using deep learning [PDF]
, 2020 Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we
Caruana, Dennis +8 more
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Diagnostics, 2022 Introduction and Background: Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis.
Chiagoziem C. Ukwuoma +5 more
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Similar image search for histopathology: SMILY [PDF]
npj Digital Medicine, 2019 AbstractThe increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Although these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location
Narayan Hegde +13 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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Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks
Diagnostics, 2023 Breast cancer is responsible for the deaths of thousands of women each year. The diagnosis of breast cancer (BC) frequently makes the use of several imaging techniques.
Ahsan Rafiq +6 more
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