Mateusz Buda
Overview
Explore the profile of Mateusz Buda including associated specialties, affiliations and a list of published articles.
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Articles
11
Citations
560
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Recent Articles
1.
Weng J, Wildman-Tobriner B, Buda M, Yang J, Ho L, Allen B, et al.
Clin Imaging
. 2023 Apr;
99:60-66.
PMID: 37116263
Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. Methods: Prior study presented...
2.
Konz N, Buda M, Gu H, Saha A, Yang J, Chledowski J, et al.
JAMA Netw Open
. 2023 Feb;
6(2):e230524.
PMID: 36821110
Importance: An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide. Objectives: To...
3.
Buda M, Saha A, Walsh R, Ghate S, Li N, Swiecicki A, et al.
JAMA Netw Open
. 2021 Aug;
4(8):e2119100.
PMID: 34398205
Importance: Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging...
4.
Swiecicki A, Konz N, Buda M, Mazurowski M
Sci Rep
. 2021 May;
11(1):10276.
PMID: 33986361
Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing...
5.
Buda M, AlBadawy E, Saha A, Mazurowski M
Radiol Artif Intell
. 2021 May;
2(1):e180050.
PMID: 33937809
Purpose: To employ deep learning to predict genomic subtypes of lower-grade glioma (LLG) tumors based on their appearance at MRI. Materials And Methods: Imaging data from The Cancer Imaging Archive...
6.
Buda M, Wildman-Tobriner B, Castor K, Hoang J, Mazurowski M
Ultrasound Med Biol
. 2019 Nov;
46(2):415-421.
PMID: 31699547
Computer-aided segmentation of thyroid nodules in ultrasound imaging could assist in their accurate characterization. In this study, using data for 1278 nodules, we proposed and evaluated two methods for deep...
7.
Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists
Buda M, Wildman-Tobriner B, Hoang J, Thayer D, Tessler F, Middleton W, et al.
Radiology
. 2019 Jul;
292(3):695-701.
PMID: 31287391
BackgroundManagement of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligence system that uses deep learning may improve radiology workflow for management of...
8.
Wildman-Tobriner B, Buda M, Hoang J, Middleton W, Thayer D, Short R, et al.
Radiology
. 2019 May;
292(1):112-119.
PMID: 31112088
Background Risk stratification systems for thyroid nodules are often complicated and affected by low specificity. Continual improvement of these systems is necessary to reduce the number of unnecessary thyroid biopsies....
9.
Buda M, Saha A, Mazurowski M
Comput Biol Med
. 2019 May;
109:218-225.
PMID: 31078126
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics...
10.
Mazurowski M, Buda M, Saha A, Bashir M
J Magn Reson Imaging
. 2018 Dec;
49(4):939-954.
PMID: 30575178
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have...