Results 11 to 20 of about 202,762 (305)
Graph walks for classification of histopathological images [PDF]
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013 This paper reports a new structural approach for automated classification of histopathological tissue images. It has two main contributions: First, unlike previous structural approaches that use a single graph for representing a tissue image, it proposes to obtain a set of subgraphs through graph walking and use these subgraphs in representing the ...
Gulden Olgun +2 more
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Oral squamous cell carcinoma detection using EfficientNet on histopathological images
Frontiers in MedicineIntroductionOral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and
Eid Albalawi +7 more
doaj +3 more sources
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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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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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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A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images [PDF]
, 2023 One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very crucial in saving lives.
Deo, Ravinesh C. +20 more
core +1 more source
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
openaire +2 more sources