Results 1 to 10 of about 259,250 (296)
Utilizing Detectron2 for accurate and efficient colon cancer detection in histopathological images [PDF]
Frontiers in Bioengineering and BiotechnologyIntroductionColon cancer ranks among the most prevalent and lethal cancers globally, emphasizing the urgent need for accurate and early diagnostic tools.
Luxi Chen +10 more
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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
core +4 more sources
Classification and localization of gastric cancer using Multi-Information Fusion Network [PDF]
Revista Română de Informatică și Automatică, 2023 Diagnosing and differentiating gastric cancer cells from stomach ulcers requires high-domain expertise and is time-consuming. Furthermore, medical image processing requires extremely high segmentation accuracy, which may lack interpretability and ...
Varghese Sicily Felix ENIGO +3 more
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A Robust Deep Learning-Based Approach for Detection of Breast Cancer from Histopathological Images
Engineering Proceedings, 2023 Breast cancer is a frequently encountered and potentially lethal illness that can affect not only women but also men. It is the most common disease affecting women globally, and is the main cause of morbidity and death.
Raheel Zaman +3 more
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Diagnostics, 2022 Targeted therapy is an effective treatment for non-small cell lung cancer. Before treatment, pathologists need to confirm tumor morphology and type, which is time-consuming and highly repetitive. In this study, we propose a multi-task deep learning model
Zhao Wang +9 more
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Results in Engineering, 2023 Lung cancer is characterized by the uncontrollable growth of cells in the lung tissues. Early diagnosis of malignant cells in the lungs, which provide oxygen to the human body and excrete carbon dioxide because of important processes, is critical ...
Vani Rajasekar +4 more
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CHILDHOOD MEDULLOBLASTOMA DIAGNOSIS USING MULTISCALE FRAMEWORK
International Journal of Advances in Signal and Image Sciences, 2022 This paper proposes an efficient Shearlet Based Childhood MedulloBlastoma (SBCMB) detection system. It is a classification system that extracts prominent characteristics for childhood MedulloBlastoma diagnosis from a given collection of histopathological
Vishal Eswaran, Usha Eswaran
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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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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
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