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Mateusz Buda

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Neural Networks : the Official Journal of the International Neural Network Society|August 10, 2018
A systematic study of the class imbalance problem in convolutional neural networksMateusz Buda, Atsuto Maki, Maciej A Mazurowski
Computers in Biology and Medicine|May 12, 2019
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmMateusz Buda, Ashirbani Saha, Maciej A Mazurowski
Scientific Reports|May 14, 2021
A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesisAlbert Swiecicki, Nicholas Konz, Mateusz Buda, et al.
Journal of Magnetic Resonance Imaging : JMRI|December 22, 2018
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRIMaciej A Mazurowski, Mateusz Buda, Ashirbani Saha, et al.
Radiology. Artificial Intelligence|May 3, 2021
Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR ImagesMateusz Buda, Ehab A AlBadawy, Ashirbani Saha, et al.
Ultrasound in Medicine & Biology|November 9, 2019
Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the ImagesMateusz Buda, Benjamin Wildman-Tobriner, Kerry Castor, et al.
JAMA Network Open|August 16, 2021
A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis ImagesMateusz Buda, Ashirbani Saha, Ruth Walsh, et al.
Radiology|July 10, 2019
Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of RadiologistsMateusz Buda, Benjamin Wildman-Tobriner, Jenny K Hoang, et al.
Radiology|May 22, 2019
Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and UtilityBenjamin Wildman-Tobriner, Mateusz Buda, Jenny K Hoang, et al.
Clinical Imaging|April 28, 2023
Deep learning for classification of thyroid nodules on ultrasound: validation on an independent datasetJingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Neural Networks : the Official Journal of the International Neural Network Society|August 10, 2018
A systematic study of the class imbalance problem in convolutional neural networksMateusz Buda, Atsuto Maki, Maciej A Mazurowski
Computers in Biology and Medicine|May 12, 2019
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmMateusz Buda, Ashirbani Saha, Maciej A Mazurowski
Scientific Reports|May 14, 2021
A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesisAlbert Swiecicki, Nicholas Konz, Mateusz Buda, et al.
Journal of Magnetic Resonance Imaging : JMRI|December 22, 2018
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRIMaciej A Mazurowski, Mateusz Buda, Ashirbani Saha, et al.
Radiology. Artificial Intelligence|May 3, 2021
Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR ImagesMateusz Buda, Ehab A AlBadawy, Ashirbani Saha, et al.
Ultrasound in Medicine & Biology|November 9, 2019
Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the ImagesMateusz Buda, Benjamin Wildman-Tobriner, Kerry Castor, et al.
JAMA Network Open|August 16, 2021
A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis ImagesMateusz Buda, Ashirbani Saha, Ruth Walsh, et al.
Radiology|July 10, 2019
Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of RadiologistsMateusz Buda, Benjamin Wildman-Tobriner, Jenny K Hoang, et al.
Radiology|May 22, 2019
Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and UtilityBenjamin Wildman-Tobriner, Mateusz Buda, Jenny K Hoang, et al.
Clinical Imaging|April 28, 2023
Deep learning for classification of thyroid nodules on ultrasound: validation on an independent datasetJingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda, et al.
Pageof 2