What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Radiology
- Internal medicine
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.
His most cited work include:
- Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis. (334 citations)
- Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images (317 citations)
- Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography (283 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (57.35%)
- Computer-aided diagnosis (36.94%)
- Mammography (30.61%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (57.35%)
- Pattern recognition (27.76%)
- Deep learning (6.33%)
In recent papers he was focusing on the following fields of study:
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.
Between 2014 and 2021, his most popular works were:
- Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets (152 citations)
- Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography (138 citations)
- Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Radiology
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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