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, Pattern recognition, Mammography, Radiology and Computer-aided diagnosis.
His Artificial intelligence study incorporates themes from Cancer and Pathology.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Image processing, Feature and Data set.
His Radiology research integrates issues from Lung and Nuclear medicine.
His Computer-aided diagnosis research includes elements of Digital mammography and Medical imaging.
His Linear discriminant analysis research is multidisciplinary, relying on both Classifier, Feature selection and Receiver operating characteristic.
His most cited work include:
- Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system (282 citations)
- A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis (254 citations)
- Deep learning in medical imaging and radiation therapy. (208 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer-aided diagnosis, Pattern recognition, Computer vision and Mammography.
His research on Artificial intelligence often connects related areas such as Receiver operating characteristic.
The concepts of his Computer-aided diagnosis study are interwoven with issues in Image segmentation, Angiography, Nuclear medicine and Medical imaging.
The various areas that he examines in his Pattern recognition study include Feature and Test set.
His Computer vision study combines topics from a wide range of disciplines, such as Microcalcification and Digital Breast Tomosynthesis.
His work deals with themes such as Malignancy and Lung, which intersect with Radiology.
He most often published in these fields:
- Artificial intelligence (58.29%)
- Computer-aided diagnosis (40.00%)
- Pattern recognition (29.14%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (58.29%)
- Pattern recognition (29.14%)
- Deep learning (8.00%)
In recent papers he was focusing on the following fields of study:
Lubomir M. Hadjiiski spends much of his time researching Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Convolutional neural network.
His study explores the link between Artificial intelligence and topics such as Computer vision that cross with problems in Digital Breast Tomosynthesis.
His research integrates issues of Random forest, Mammography, Digital mammography and Feature in his study of Pattern recognition.
His study looks at the intersection of Segmentation and topics like Bladder cancer with Radiology, Stage, Treatment response, Linear discriminant analysis and Nuclear medicine.
Lubomir M. Hadjiiski has included themes like Decision support system and Receiver operating characteristic in his Radiology study.
His Image segmentation research includes elements of Artery and Malignancy.
Between 2015 and 2021, his most popular works were:
- Deep learning in medical imaging and radiation therapy. (208 citations)
- 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)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Cancer
- Machine learning
Lubomir M. Hadjiiski focuses on Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Segmentation.
His study ties his expertise on Digital mammography together with the subject of Artificial intelligence.
His work carried out in the field of Pattern recognition brings together such families of science as Random forest, Mammography, Digital Breast Tomosynthesis and Feature.
His Segmentation research is multidisciplinary, incorporating perspectives in Bladder cancer, Stability, Jaccard index and Margin.
His study with Computer-aided diagnosis involves better knowledge in Radiology.
Lubomir M. Hadjiiski interconnects Feature extraction and Feature selection in the investigation of issues within Linear discriminant analysis.
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