Results 11 to 20 of about 259,250 (296)

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., Boucheron, Laura E., Can, Ali, Madabhushi, Anant, Rajpoot, Nasir M., Yener, Bülent   +5 more
openaire   +3 more sources

Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images

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, Min Li, Panyun Zhou, Xin Deng, Siyu Li, Xingyue Zhao, Yi Wu, Jiwei Qin, Wenjia Guo   +8 more
doaj   +1 more source

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, Chuang, Jeffrey H., Farahmand, Saman, Foroughi pour, Ali, Namburi, Sandeep, Noorbakhsh, Javad, Rimm, David, Soltanieh-Ha, Mohammad, Zarringhalam, Kourosh   +8 more
core   +1 more source

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, Jason D. Hipp, Yun Liu, Michael Emmert-Buck, Emily Reif, Daniel Smilkov, Michael Terry, Carrie J. Cai, Mahul B. Amin, Craig H. Mermel, Phil Q. Nelson, Lily H. Peng, Greg S. Corrado, Martin C. Stumpe   +13 more
openaire   +3 more sources

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, Yongjie Han, Wuyi Ming, Jinguang Du, Yinxia Liu, Yuan Yang, Leijie Wang, Yongqiang Wang, Zhiwen Jiang, Chen Cao, Jie Yuan   +10 more
doaj   +1 more source

Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head

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, Md Altab Hossain, Jehoiada K. Jackson, Grace U. Nneji, Happy N. Monday, Zhiguang Qin   +5 more
doaj   +1 more source

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

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, Mohammed M. Abdelsamea, Mona Mostafa Mohamed, Mohamed Medhat Gaber   +3 more
doaj   +1 more source

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, Alexander Chursin, Wejdan Awad Alrefaei, Tahani Rashed Alsenani, Ghadah Aldehim, Nagwan Abdel Samee, Leila Jamel Menzli   +6 more
doaj   +1 more source

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, Meziat, Carole, Loménie, Nicolas   +2 more
openaire   +2 more sources

The accurate staging of ovarian cancer using 3T magnetic resonance imaging - a realistic option [PDF]

, 2008
Objectives: The aim of the study was to determine whether staging primary ovarian cancer using 3.0 Tesla (3T) magnetic resonance imaging (MRI) is comparable to surgical staging of the disease.
Booth, SJ, Poole, DR, Richmond, I, Turnbull, LW   +3 more
core   +1 more source