What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Cancer
- Radiology
His primary scientific interests are in Artificial intelligence, Breast ultrasound, Pattern recognition, Computer-aided diagnosis and Pathology.
His studies in Artificial intelligence integrate themes in fields like Automated whole-breast ultrasound and Computer vision.
The Breast ultrasound study combines topics in areas such as BI-RADS, Breast imaging and Speckle pattern.
Ruey-Feng Chang combines subjects such as Breast cancer and Predictive value with his study of Computer-aided diagnosis.
Ruey-Feng Chang has included themes like Image quality, Radiology, Ultrasound and Receiver operating characteristic in his Pathology study.
His Radiology study incorporates themes from Cancer and Surgery.
His most cited work include:
- Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. (178 citations)
- Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks (173 citations)
- Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. (167 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Radiology, Pattern recognition, Breast cancer and Computer-aided diagnosis.
His research on Artificial intelligence often connects related topics like Computer vision.
His Radiology research includes themes of Cancer, Mammography, Breast ultrasound and Receiver operating characteristic.
He has researched Receiver operating characteristic in several fields, including BI-RADS, Breast imaging, Region growing and Pathology.
The various areas that he examines in his Breast cancer study include Segmentation, Metastasis and Cluster analysis.
His Computer-aided diagnosis study which covers Second opinion that intersects with Predictive value of tests.
He most often published in these fields:
- Artificial intelligence (47.56%)
- Radiology (44.51%)
- Pattern recognition (33.54%)
What were the highlights of his more recent work (between 2014-2021)?
- Breast cancer (28.66%)
- Radiology (44.51%)
- Computer-aided diagnosis (29.88%)
In recent papers he was focusing on the following fields of study:
Ruey-Feng Chang mainly focuses on Breast cancer, Radiology, Computer-aided diagnosis, Artificial intelligence and Ultrasound.
His Breast cancer research is multidisciplinary, relying on both Segmentation and Pathology.
Ruey-Feng Chang interconnects Whole breast, Lymph node, Primary tumor, Metastasis and Electromagnetic tracking in the investigation of issues within Radiology.
His studies deal with areas such as Feature, Texture, Speckle pattern, Elastography and Receiver operating characteristic as well as Computer-aided diagnosis.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Lung cancer and Pattern recognition.
His research integrates issues of Rotator cuff, Breast density and Physical examination in his study of Ultrasound.
Between 2014 and 2021, his most popular works were:
- Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation (46 citations)
- Computer-aided diagnosis of liver tumors on computed tomography images (44 citations)
- Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI. (39 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Cancer
- Internal medicine
His primary areas of investigation include Breast cancer, Computer-aided diagnosis, Pattern recognition, Artificial intelligence and Receiver operating characteristic.
The study incorporates disciplines such as Feature and Pathology in addition to Breast cancer.
His Pathology research is multidisciplinary, incorporating elements of Mammography and Ultrasound.
He works mostly in the field of Pattern recognition, limiting it down to concerns involving Breast ultrasound and, occasionally, Feature extraction, Convolutional neural network and Image fusion.
His study connects Computer vision and Artificial intelligence.
His biological study spans a wide range of topics, including BI-RADS, Radiology, Oncology and Speckle pattern.
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