Results 151 to 160 of about 313,987 (188)

ON THE PROBLEM OF VALIDATING COMPUTER ASSISTED MEDICAL IMAGE INTERPRETATION

1997
Hinging on approaches from the fields of clinical imaging and information technology assessment in medicine, a validation scheme is presented for 3-D biomedical image interpretation. This scheme considers measurement of algorithmic accuracy and evaluation of clinical functionality and usefulness.
null Van Cleynenbreugel Johan, null Nuyts Johan, null Verbeeck Rudi, null Smet Maria-Helena, null Marchal Guy, null Suetens Paul   +5 more
openaire   +1 more source

Abstract 15456: Computer-assisted Diagnosis of Coronary Angiography

Circulation, 2015
Introduction: Current analysis of coronary angiograms (CAs) relies on visual estimation by human operators with several limitations. Techniques like quantitative coronary angiography are available but require significant user input.
S. Soroushmehr, A. Bafna, Sinha Shashank, Nallamothu Brahmajee, Kevin Ward, K. Najarian   +5 more
semanticscholar   +1 more source

[Computer-assisted diagnosis based on computer-based image interpretation and 3D-visualization].

Der Radiologe, 1998
To survey methods for 3D data visualization and image analysis which can be used for computer based diagnostics.The methods available are explained in short terms and links to the literature are presented. Methods which allow basic manipulation of 3D data are windowing, rotation and clipping.
D, Schuhmann, M, Seemann, U J, Schoepf, M, Haubner, C, Krapichler, K, Gebicke, M, Reiser, K H, Englmeier   +7 more
openaire   +1 more source

Computer-assisted image interpretation: use of a neural network to differentiate tubular carcinoma from sclerosing adenosis.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 1992
Measurement of nuclear and glandular size and shape features was carried out on 18 cases of sclerosing adenosis and 18 cases of tubular carcinoma. Modified Bonferroni analysis showed that glandular surface density and the coefficient of variation of luminal form factor were significant in discriminating between these two lesions.
T J, O'Leary, U V, Mikel, R L, Becker
openaire   +1 more source

Smart Investigations into the Development of an Effective Computer-Assisted Diagnosis System for CT Scan Brain Depictions

SN Computer Science, 2023
Sasidhar Attuluri, Bhupati Ch, L. Ramya, A. Tiwari, Raja Rao Budaraju, J. Cotrina-Aliaga   +5 more
semanticscholar   +1 more source

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv.org
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation.
Hongwei Bran, Fernando Navarro, I. Ezhov, Amirhossein Bayat, Dhritiman Das, F. Kofler, Suprosanna Shit, Diana Waldmannstetter, J. Paetzold, Xiaobin Hu, Benedikt Wiestler, Lucas Zimmer, Tamaz Amiranashvili, Chinmay Prabhakar, Christoph Berger, J. Weidner, Michelle Alonso-Basant, Arif Rashid, U. Baid, Wesam Adel, Deniz Ali, B. Baheti, Ying-Long Bai, Ishaan Bhatt, Sabri Can Cetindag, Wenting Chen, Li Cheng, Prasad Dutand, Lara Dular, M. Elattar, Ming Feng, Shengbo Gao, Henkjan Huisman, Weifeng Hu, S. Innani, Wei Jiat, Davood Karimi, Hugo J. Kuijf, Jin Tae Kwak, H. Le, Xiang Lia, Huiyan Lin, Tongliang Liu, Jun Ma, Kai Ma, Ting Ma, I. Oksuz, Robbie Holland, Arlindo L. Oliveira, Jimut Bahan Pal, Xuan Pei, Maoying Qiao, A. Saha, Raghavendra Selvan, Linlin Shen, Joao Lourencco Silva, Žiga Špiclin, S. Talbar, Dadong Wang, Wei Wang, Xiong Wang, Yin Wang, Ruiling Xia, Kele Xu, Yanwu Yan, M. Yergin, Shuang Yu, Lingxi Zeng, Yinglin Zhang, Jiachen Zhao, Yefeng Zheng, Martin Zukovec, Richard Do, Anton S. Becker, Amber L. Simpson, E. Konukoglu, A. Jakab, S. Bakas, Leo Joskowicz, Bjoern H Menze   +79 more
semanticscholar   +1 more source

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020
Anne L. Martel, P. Abolmaesumi, D. Stoyanov, Diana Mateus, Maria A. Zuluaga, S. K. Zhou, Daniel Racoceanu, Leo Joskowicz   +7 more
semanticscholar   +1 more source

S898 Empirical Mode Decomposition Based Deep Learning Algorithm for Gastrointestinal Endoscopic Image Classification

American Journal of Gastroenterology
Introduction: GI endoscopy encompasses diagnostic and therapeutic procedures to image, assess, and treat GI illnesses. Large volumes of images and videos generated during these procedures makes image interpretation cumbersome.
Keerthy Gopalakrishnan, Charmy Parikh, Aastha Goudel, Divyanshi Sood, S. Arunachalam, Mou Deb, Mrinal Kanti   +6 more
semanticscholar   +1 more source

Hermeneutica: Computer-Assisted Interpretation in the Humanities

, 2016
Geoffrey Rockwell, S. Sinclair
semanticscholar   +1 more source

Sa2029 Computer-Assisted Interpretation of the NICE Criteria for Colorectal Polyps using Near-Focus Narrow-Band Imaging

Gastroenterology, 2016
Eladio Rodriguez-Diaz, Satish K. Singh
openaire   +2 more sources