His primary areas of study are Artificial intelligence, Mammography, Pattern recognition, Computer-aided diagnosis and Radiology. He interconnects Computer vision and Receiver operating characteristic in the investigation of issues within Artificial intelligence. Heang Ping Chan has researched Mammography in several fields, including Image processing, Pixel and Image quality.
The Pattern recognition study combines topics in areas such as Contextual image classification, Cancer, Data set and Thresholding. His biological study spans a wide range of topics, including Lung cancer, Medical physics, Computed tomography and Medical imaging. His work in Radiology tackles topics such as Nuclear medicine which are related to areas like Radiography and Tomography.
Heang Ping Chan spends much of his time researching Artificial intelligence, Computer-aided diagnosis, Mammography, Computer vision and Pattern recognition. His Artificial intelligence research is multidisciplinary, relying on both Digital mammography and Receiver operating characteristic. His Computer-aided diagnosis research incorporates themes from Region growing, Image segmentation, Nuclear medicine and Medical imaging.
His Mammography study frequently links to other fields, such as Image processing. His studies in Computer vision integrate themes in fields like Microcalcification and Digital Breast Tomosynthesis. His Pattern recognition research integrates issues from Artificial neural network and Test set.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Deep learning, Computer-aided diagnosis and Convolutional neural network. His research brings together the fields of Computer vision and Artificial intelligence. His Pattern recognition study incorporates themes from Feature and Mammography, Digital Breast Tomosynthesis, Digital mammography.
He studied Deep learning and Pixel that intersect with Euclidean distance. His Computer-aided diagnosis research includes themes of Ureter, Angiography and Receiver operating characteristic. His study looks at the relationship between Segmentation and fields such as Bladder cancer, as well as how they intersect with chemical problems.
Heang Ping Chan mainly focuses on Artificial intelligence, Convolutional neural network, Pattern recognition, Deep learning and Segmentation. His study connects Mammography and Artificial intelligence. His study on Digital mammography, Mammographic breast density and Breast density is often connected to MEDLINE as part of broader study in Mammography.
His Pattern recognition study combines topics from a wide range of disciplines, such as Random forest and Feature. In his study, which falls under the umbrella issue of Deep learning, Data science is strongly linked to Decision support system. His study in Segmentation is interdisciplinary in nature, drawing from both Bladder cancer and Voxel.
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Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography
Heang Ping Chan;Kunio Doi;Simranjit Galhotra;Carl J. Vyborny.
Medical Physics (1987)
Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.
H P Chan;K Doi;C J Vyborny;R A Schmidt.
Investigative Radiology (1990)
Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
B. Sahiner;Heang-Ping Chan;N. Petrick;Datong Wei.
IEEE Transactions on Medical Imaging (1996)
Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system
Metin N. Gurcan;Berkman Sahiner;Nicholas Petrick;Heang Ping Chan.
Medical Physics (2002)
A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis
Yiheng Zhang;Heang Ping Chan;Berkman Sahiner;Jun Wei.
Medical Physics (2006)
Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.
Maryellen L. Giger;Heang Ping Chan;John M Boone.
Medical Physics (2008)
Artificial convolution neural network for medical image pattern recognition
Shih-Chung B. Lo;Heang-Ping Chan;Jyh-Shyan Lin;Huai Li.
Neural Networks (1995)
Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.
Heang-Ping Chan;Berkman Sahiner;Mark A. Helvie;Nicholas Petrick.
Evaluation of the transmitted exposure through lead equivalent aprons used in a radiology department, including the contribution from backscatter.
Emmanuel G. Christodoulou;Mitchell M. Goodsitt;Sandra C. Larson;Katie L. Darner.
Medical Physics (2003)
An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection
N. Petrick;Heang-Ping Chan;B. Sahiner;Datong Wei.
IEEE Transactions on Medical Imaging (1996)
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