2019 - Fellow of the Indian National Academy of Engineering (INAE)
Ronald M. Summers mainly investigates Artificial intelligence, Radiology, Segmentation, Pattern recognition and Computer vision. Ronald M. Summers regularly links together related areas like Machine learning in his Artificial intelligence studies. Ronald M. Summers works mostly in the field of Machine learning, limiting it down to topics relating to Image and, in certain cases, Recurrent neural network.
As part of one scientific family, Ronald M. Summers deals mainly with the area of Radiology, narrowing it down to issues related to the Colonoscopy, and often Lumen. When carried out as part of a general Segmentation research project, his work on Image segmentation and Sørensen–Dice coefficient is frequently linked to work in Prior probability, therefore connecting diverse disciplines of study. His work on Conditional random field and k-nearest neighbors algorithm as part of general Pattern recognition study is frequently linked to Gaussian blur, Digital subscriber line and Network architecture, therefore connecting diverse disciplines of science.
The scientist’s investigation covers issues in Artificial intelligence, Radiology, Pattern recognition, Segmentation and Computer vision. His biological study spans a wide range of topics, including Virtual colonoscopy and Machine learning. His study on Computer-aided diagnosis is often connected to Computer aided detection as part of broader study in Radiology.
His Computer-aided diagnosis study combines topics from a wide range of disciplines, such as Cancer and CAD. His research integrates issues of Artificial neural network, Image and Feature in his study of Pattern recognition. His Segmentation study incorporates themes from Lesion, Random forest and Computed tomography.
His primary areas of study are Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Medical imaging. His Artificial intelligence research incorporates elements of Machine learning, Computed tomography and Natural language processing. The concepts of his Pattern recognition study are interwoven with issues in Domain, Artificial neural network and Surgical planning.
His studies in Segmentation integrate themes in fields like Lesion, Cylinder, Pixel, Translation and Radiology. His work in the fields of Radiology, such as Abdominal ct, overlaps with other areas such as Response Evaluation Criteria in Solid Tumors. The Medical imaging study combines topics in areas such as Semi-supervised learning, Image segmentation, Radiography, Anomaly detection and Data science.
Ronald M. Summers spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Medical imaging and Radiology. His Artificial intelligence study combines topics in areas such as Pneumonia and Lung disease. Ronald M. Summers combines subjects such as Feature, Deep neural networks, Regularization, Representation and Image with his study of Pattern recognition.
His Segmentation research includes themes of Pixel, Lesion detection and Computed tomography. His work deals with themes such as Semi-supervised learning, Training set, Artificial neural network, Field and Convolutional neural network, which intersect with Medical imaging. His work carried out in the field of Radiology brings together such families of science as Prostate, Prostate cancer, Multiparametric MRI and Cohort.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin;Holger R. Roth;Mingchen Gao;Le Lu.
IEEE Transactions on Medical Imaging (2016)
Polyp Size Measurement at CT Colonography: What Do We Know and What Do We Need to Know?
Ronald M. Summers.
Polyps: Linear and Volumetric Measurement at CT Colonography
Srinath C. Yeshwant;Ronald M. Summers;Jianhua Yao;Daniel S. Brickman.
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Xiaosong Wang;Yifan Peng;Le Lu;Zhiyong Lu.
computer vision and pattern recognition (2017)
Machine learning and radiology
Shijun Wang;Ronald M. Summers.
Medical Image Analysis (2012)
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
Holger R. Roth;Le Lu;Jiamin Liu;Jianhua Yao.
IEEE Transactions on Medical Imaging (2016)
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello.
arXiv: Computer Vision and Pattern Recognition (2019)
Deep learning in medical imaging and radiation therapy.
Berkman Sahiner;Aria Pezeshk;Lubomir M. Hadjiiski;Xiaosong Wang.
Medical Physics (2019)
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Holger R. Roth;Le Lu;Amal Farag;Hoo-Chang Shin.
medical image computing and computer assisted intervention (2015)
The future of digital health with federated learning
Nicola Rieke;Nicola Rieke;Jonny Hancox;Wenqi Li;Fausto Milletari.
npj Digital Medicine (2020)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: