Maciej A. Mazurowski
Orcid: 0000-0003-4202-8602
According to our database1,
Maciej A. Mazurowski
authored at least 90 papers
between 2005 and 2024.
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Bibliography
2024
Pattern Recognit., February, 2024
CoRR, 2024
CoRR, 2024
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation.
CoRR, 2024
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models.
CoRR, 2024
CoRR, 2024
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images.
CoRR, 2024
2023
SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images.
IEEE Trans. Medical Imaging, December, 2023
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach.
J. Digit. Imaging, December, 2023
Medical Image Anal., October, 2023
J. Digit. Imaging, April, 2023
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion.
Medical Image Anal., 2023
How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model.
CoRR, 2023
A systematic study of the foreground-background imbalance problem in deep learning for object detection.
CoRR, 2023
Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation.
Artif. Intell. Medicine, 2023
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images.
Proceedings of the Medical Imaging with Deep Learning, 2023
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs.
Proceedings of the Medical Imaging with Deep Learning, 2023
2022
IEEE Trans. Biomed. Eng., 2022
IEEE Trans. Pattern Anal. Mach. Intell., 2022
Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.
BMC Medical Informatics Decis. Mak., 2022
Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset.
CoRR, 2022
Automated Grading of Radiographic Knee Osteoarthritis Severity Combined with Joint Space Narrowing.
CoRR, 2022
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT.
CoRR, 2022
Proceedings of the Cancer Prevention Through Early Detection, 2022
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022
Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
2021
Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.
Medical Image Anal., 2021
REPLICA: Enhanced Feature Pyramid Network by Local Image Translation and Conjunct Attention for High-Resolution Breast Tumor Detection.
CoRR, 2021
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning.
CoRR, 2021
Comput. Methods Programs Biomed., 2021
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists.
Comput. Biol. Medicine, 2021
Deep neural networks trained for segmentation are sensitive to brightness changes: preliminary results.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021
2020
Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation.
IEEE Trans. Biomed. Eng., 2020
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5, 060 patients and a deep learning model.
CoRR, 2020
CoRR, 2020
Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
2019
Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics.
IEEE Trans. Medical Imaging, 2019
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.
Comput. Biol. Medicine, 2019
Comput. Biol. Medicine, 2019
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.
Comput. Biol. Medicine, 2019
Malignant microcalcification clusters detection using unsupervised deep autoencoders.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
2018
Neural Networks, 2018
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images.
CoRR, 2018
Deep learning in radiology: an overview of the concepts and a survey of the state of the art.
CoRR, 2018
Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Breast cancer molecular subtype classification using deep features: preliminary results.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
2017
Expert Syst. Appl., 2017
Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
Deep learning for segmentation of brain tumors: can we train with images from different institutions?
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
2016
Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.
Expert Syst. Appl., 2016
A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases.
Expert Syst. Appl., 2016
Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features.
Proceedings of the Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, San Diego, California, United States, 27 February, 2016
Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data.
Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February, 2016
Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype.
Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February, 2016
2015
Modeling false positive error making patterns in radiology trainees for improved mammography education.
J. Biomed. Informatics, 2015
2013
Estimating confidence of individual rating predictions in collaborative filtering recommender systems.
Expert Syst. Appl., 2013
2012
The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support.
Neural Networks, 2012
2011
Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis.
J. Biomed. Informatics, 2011
2010
Perception-driven IT-CADe analysis for the detection of masses in screening mammography: initial investigation.
Proceedings of the Medical Imaging 2010: Computer-Aided Diagnosis, 2010
2009
Building virtual community in computational intelligence and machine learning [Research Frontier].
IEEE Comput. Intell. Mag., 2009
Relational representation for improved decisions with an information-theoretic CADe system: initial experience.
Proceedings of the Medical Imaging 2009: Computer-Aided Diagnosis, 2009
A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results.
Proceedings of the Medical Imaging 2009: Computer-Aided Diagnosis, 2009
Evaluating classifiers: Relation between area under the receiver operator characteristic curve and overall accuracy.
Proceedings of the International Joint Conference on Neural Networks, 2009
The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems.
Proceedings of the International Joint Conference on Neural Networks, 2009
2008
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance.
Neural Networks, 2008
Database decomposition of a knowledge-based CAD system in mammography: an ensemble approach to improve detection.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008
Proceedings of the Digital Mammography, 2008
Proceedings of the International Joint Conference on Neural Networks, 2008
2007
Proceedings of the 2007 IEEE Swarm Intelligence Symposium, 2007
Stacked Generalization in Computer-Assisted Decision Systems: Empirical Comparison of Data Handling Schemes.
Proceedings of the International Joint Conference on Neural Networks, 2007
Impact of Low Class Prevalence on the Performance Evaluation of Neural Network Based Classifiers: Experimental Study in the Context of Computer-Assisted Medical Diagnosis.
Proceedings of the International Joint Conference on Neural Networks, 2007
Proceedings of the IEEE Congress on Evolutionary Computation, 2007
Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms.
Proceedings of the IEEE Congress on Evolutionary Computation, 2007
2005
Proceedings of the Computer Aided Systems Theory, 2005