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Maciej A. Mazurowski
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2020 – today
- 2024
- [j28]Yixin Zhang
, Maciej A. Mazurowski:
Convolutional neural networks rarely learn shape for semantic segmentation. Pattern Recognit. 146: 110018 (2024) - [c46]Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski:
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation. CVPR Workshops 2024: 5046-5055 - [c45]Nicholas Konz, Maciej A. Mazurowski:
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images. ICLR 2024 - [c44]Nicholas Konz, Yuwen Chen, Haoyu Dong, Maciej A. Mazurowski:
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models. MICCAI (7) 2024: 88-98 - [i44]Nicholas Konz
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images. CoRR abs/2401.08865 (2024) - [i43]Hanxue Gu, Roy J. Colglazier, Haoyu Dong, Jikai Zhang, Yaqian Chen, Zafer Yildiz, Yuwen Chen, Lin Li, Jichen Yang, Jay Willhite, Alex M. Meyer, Brian Guo, Yashvi Atul Shah, Emily Luo, Shipra Rajput, Sally Kuehn, Clark Bulleit, Kevin A. Wu, Jisoo Lee, Brandon Ramirez, Darui Lu, Jay M. Levin
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI. CoRR abs/2401.12974 (2024) - [i42]Nicholas Konz
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models. CoRR abs/2402.05210 (2024) - [i41]Haoyu Dong, Nicholas Konz
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation. CoRR abs/2402.09604 (2024) - [i40]Yuwen Chen, Nicholas Konz, Hanxue Gu, Haoyu Dong, Yaqian Chen, Lin Li, Jisoo Lee, Maciej A. Mazurowski:
ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models. CoRR abs/2403.10786 (2024) - [i39]Keyu Li, Hanxue Gu, Roy J. Colglazier, Robert Lark, Elizabeth Hubbard, Robert French, Denise Smith, Jikai Zhang, Erin McCrum, Anthony Catanzano, Joseph Cao, Leah Waldman, Maciej A. Mazurowski, Benjamin Alman:
Deep learning automates Cobb angle measurement compared with multi-expert observers. CoRR abs/2403.12115 (2024) - [i38]Nicholas Konz, Yuwen Chen, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski:
Rethinking Perceptual Metrics for Medical Image Translation. CoRR abs/2404.07318 (2024) - [i37]Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski:
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model. CoRR abs/2404.09957 (2024) - [i36]Haoyu Dong, Hanxue Gu, Yaqian Chen, Jichen Yang, Yuwen Chen, Maciej A. Mazurowski:
Segment anything model 2: an application to 2D and 3D medical images. CoRR abs/2408.00756 (2024) - [i35]Nicholas Konz, Maciej A. Mazurowski:
Pre-processing and Compression: Understanding Hidden Representation Refinement Across Imaging Domains via Intrinsic Dimension. CoRR abs/2408.08381 (2024) - [i34]Zafer Yildiz, Yuwen Chen, Maciej A. Mazurowski:
SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images. CoRR abs/2408.15224 (2024) - [i33]Gregory Szumel, Brian Guo, Darui Lu, Rongze Gui, Tingyu Wang, Nicholas Konz, Maciej A. Mazurowski:
The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study. CoRR abs/2409.04368 (2024) - [i32]Mohamed Sobhi Jabal, Pranav I. Warman, Jikai Zhang, Kartikeye Gupta, Ayush Jain, Maciej A. Mazurowski, Walter F. Wiggins, Kirti Magudia, Evan Calabrese:
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports. CoRR abs/2409.10576 (2024) - 2023
- [j27]Jikai Zhang
Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation. Artif. Intell. Medicine 141: 102553 (2023) - [j26]Shixing Cao, Nicholas Konz
Deep Learning for Breast MRI Style Transfer with Limited Training Data. J. Digit. Imaging 36(2): 666-678 (2023) - [j25]Jikai Zhang
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach. J. Digit. Imaging 36(6): 2402-2410 (2023) - [j24]Nicholas Konz
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion. Medical Image Anal. 87: 102836 (2023) - [j23]Maciej A. Mazurowski, Haoyu Dong
Segment anything model for medical image analysis: An experimental study. Medical Image Anal. 89: 102918 (2023) - [j22]Haoyu Dong
SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images. IEEE Trans. Medical Imaging 42(12): 3860-3870 (2023) - [c43]Hanxue Gu, Hongyu He, Roy J. Colglazier, Jordan Axelrod, Robert French, Maciej A. Mazurowski:
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs. MIDL 2023: 119-133 - [c42]Nicholas Konz, Maciej A. Mazurowski:
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images. MIDL 2023: 829-845 - [i31]Shixing Cao, Nicholas Konz, James S. Duncan, Maciej A. Mazurowski:
Deep Learning for Breast MRI Style Transfer with Limited Training Data. CoRR abs/2301.02069 (2023) - [i30]Nicholas Konz
Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images. CoRR abs/2303.04911 (2023) - [i29]Hanxue Gu, Hongyu He, Roy J. Colglazier, Jordan Axelrod, Robert French, Maciej A. Mazurowski:
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs. CoRR abs/2303.07517 (2023) - [i28]Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang
Segment Anything Model for Medical Image Analysis: an Experimental Study. CoRR abs/2304.10517 (2023) - [i27]Nicholas Konz
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion. CoRR abs/2305.03098 (2023) - [i26]Yixin Zhang, Maciej A. Mazurowski:
Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation. CoRR abs/2305.06568 (2023) - [i25]Hanxue Gu, Haoyu Dong, Nicholas Konz, Maciej A. Mazurowski:
A systematic study of the foreground-background imbalance problem in deep learning for object detection. CoRR abs/2306.16539 (2023) - [i24]Jikai Zhang, Carlos Santos, Christine Park, Maciej A. Mazurowski, Roy J. Colglazier:
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach. CoRR abs/2309.02681 (2023) - [i23]Jee Seok Yoon, Kwanseok Oh, Yooseung Shin, Maciej A. Mazurowski, Heung-Il Suk:
Domain Generalization for Medical Image Analysis: A Survey. CoRR abs/2310.08598 (2023) - [i22]Yixin Zhang, Shen Zhao, Hanxue Gu, Maciej A. Mazurowski:
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 abs/2312.10600 (2023) - 2022
- [j21]Vincent M. D'Anniballe
Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. BMC Medical Informatics Decis. Mak. 22(1): 102 (2022) - [j20]Zhe Zhu
3D Pyramid Pooling Network for Abdominal MRI Series Classification. IEEE Trans. Pattern Anal. Mach. Intell. 44(4): 1688-1698 (2022) - [j19]Rui Hou
Anomaly Detection of Calcifications in Mammography Based on 11, 000 Negative Cases. IEEE Trans. Biomed. Eng. 69(5): 1639-1650 (2022) - [c41]Fakrul Islam Tushar
Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography. Computer-Aided Diagnosis 2022 - [c40]Yifan Zhang, Haoyu Dong, Nicholas Konz
Lightweight Transformer Backbone for Medical Object Detection. CaPTion@MICCAI 2022: 47-56 - [c39]Nicholas Konz
The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning. MICCAI (8) 2022: 684-694 - [i21]Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu, Ehsan Abadi, Maciej A. Mazurowski, Ehsan Samei, William Paul Segars, Joseph Y. Lo:
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT. CoRR abs/2203.01934 (2022) - [i20]Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima, Rafael B. Fricks, Maciej A. Mazurowski, William Paul Segars, Ehsan Samei, Joseph Y. Lo:
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography. CoRR abs/2203.03074 (2022) - [i19]Hanxue Gu, Keyu Li, Roy J. Colglazier, Jichen Yang
Automated Grading of Radiographic Knee Osteoarthritis Severity Combined with Joint Space Narrowing. CoRR abs/2203.08914 (2022) - [i18]Jikai Zhang, Maciej A. Mazurowski, Brian C. Allen, Benjamin Wildman-Tobriner:
Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation. CoRR abs/2206.14305 (2022) - [i17]Nicholas Konz, Hanxue Gu, Haoyu Dong, Maciej A. Mazurowski:
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning. CoRR abs/2207.02797 (2022) - [i16]Albert Swiecicki, Nianyi Li, Jonathan O'Donnell, Nicholas Said, Jichen Yang
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. CoRR abs/2207.12521 (2022) - [i15]Jingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda, Jichen Yang, Lisa M. Ho, Brian C. Allen, Wendy L. Ehieli, Chad M. Miller, Jikai Zhang, Maciej A. Mazurowski:
Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset. CoRR abs/2207.13765 (2022) - 2021
- [j18]Albert Swiecicki
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Comput. Biol. Medicine 133: 104334 (2021) - [j17]Gourav Modanwal
Normalization of breast MRIs using cycle-consistent generative adversarial networks. Comput. Methods Programs Biomed. 208: 106225 (2021) - [j16]Rachel Lea Draelos
Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical Image Anal. 67: 101857 (2021) - [c38]Zhe Zhu, Mustafa R. Bashir, Maciej A. Mazurowski:
Deep neural networks trained for segmentation are sensitive to brightness changes: preliminary results. Computer-Aided Diagnosis 2021 - [e2]Maciej A. Mazurowski, Karen Drukker:
Medical Imaging 2021: Computer-Aided Diagnosis, Online, February 15-20, 2021. SPIE Proceedings 11597, SPIE 2021, ISBN 9781510640238 [contents] - [i14]Vincent M. D'Anniballe, Fakrul Islam Tushar, Khrystyna Faryna, Songyue Han, Maciej A. Mazurowski, Geoffrey D. Rubin, Joseph Y. Lo:
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning. CoRR abs/2102.02959 (2021) - [i13]Maciej A. Mazurowski:
Do We Expect More from Radiology AI than from Radiologists? CoRR abs/2105.06264 (2021) - [i12]Yifan Zhang, Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski:
REPLICA: Enhanced Feature Pyramid Network by Local Image Translation and Conjunct Attention for High-Resolution Breast Tumor Detection. CoRR abs/2111.11546 (2021) - 2020
- [j15]Rui Hou
Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation. IEEE Trans. Biomed. Eng. 67(6): 1565-1572 (2020) - [c37]Rui Hou, Lars J. Grimm, Maciej A. Mazurowski, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, Eun-Sil Shelley Hwang, Joseph Y. Lo:
A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography. Computer-Aided Diagnosis 2020 - [c36]Nianyi Li, Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, William A. Jiranek, Maciej A. Mazurowski:
Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms. Computer-Aided Diagnosis 2020 - [c35]Gourav Modanwal
MRI image harmonization using cycle-consistent generative adversarial network. Computer-Aided Diagnosis 2020 - [c34]Anindo Saha
Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. Computer-Aided Diagnosis 2020 - [c33]Albert Swiecicki, Mateusz Buda, Ashirbani Saha, Nianyi Li, Sujata V. Ghate, Ruth Walsh
Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images. Computer-Aided Diagnosis 2020 - [c32]Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, Mateusz Buda, Nianyi Li, William A. Jiranek, Maciej A. Mazurowski:
Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms. Computer-Aided Diagnosis 2020 - [e1]Horst K. Hahn, Maciej A. Mazurowski:
Medical Imaging 2020: Computer-Aided Diagnosis, Houston, TX, USA, February 16-19, 2020. SPIE Proceedings 11314, SPIE 2020, ISBN 9781510633957 [contents] - [i11]Rachel Lea Draelos, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, Lawrence Carin:
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes. CoRR abs/2002.04752 (2020) - [i10]Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, Joseph Y. Lo:
Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans. CoRR abs/2008.01158 (2020) - [i9]Anindo Saha, Fakrul Islam Tushar, Khrystyna Faryna, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Geoffrey D. Rubin, Joseph Y. Lo:
Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features. CoRR abs/2011.00149 (2020) - [i8]Mateusz Buda, Ashirbani Saha, Ruth Walsh, Sujata V. Ghate, Nianyi Li, Albert Swiecicki, Joseph Y. Lo, Maciej A. Mazurowski:
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5, 060 patients and a deep learning model. CoRR abs/2011.07995 (2020)
2010 – 2019
- 2019
- [j14]Zhe Zhu, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, Maciej A. Mazurowski:
Deep learning for identifying radiogenomic associations in breast cancer. Comput. Biol. Medicine 109: 85-90 (2019) - [j13]Mateusz Buda
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. Comput. Biol. Medicine 109: 218-225 (2019) - [j12]Zhe Zhu, Michael R. Harowicz, Jun Zhang, Ashirbani Saha, Lars J. Grimm, Eun-Sil Shelley Hwang, Maciej A. Mazurowski:
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. Comput. Biol. Medicine 115 (2019) - [j11]Jun Zhang
Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics. IEEE Trans. Medical Imaging 38(2): 435-447 (2019) - [c31]Matias Benitez, James Tian, Mark Kelly, Vignesh Selvakumaran
Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports. Computer-Aided Diagnosis 2019: 109500V - [c30]Rui Hou, Yinhao Ren, Lars J. Grimm, Maciej A. Mazurowski, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, Eun-Sil Shelley Hwang, Joseph Y. Lo:
Malignant microcalcification clusters detection using unsupervised deep autoencoders. Computer-Aided Diagnosis 2019: 109502Q - [i7]Mateusz Buda, Ashirbani Saha, Maciej A. Mazurowski:
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. CoRR abs/1906.03720 (2019) - [i6]Gourav Modanwal, Adithya Vellal, Maciej A. Mazurowski:
Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks. CoRR abs/1912.08061 (2019) - 2018
- [j10]Mateusz Buda
A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks 106: 249-259 (2018) - [c29]Rui Hou, Bibo Shi, Lars J. Grimm
Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features. Computer-Aided Diagnosis 2018: 105750R - [c28]Jun Zhang, Ashirbani Saha, Zhe Zhu, Maciej A. Mazurowski:
Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics. Computer-Aided Diagnosis 2018: 105750U - [c27]Bibo Shi, Rui Hou, Maciej A. Mazurowski, Lars J. Grimm
Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning. Computer-Aided Diagnosis 2018: 105752R - [c26]Jun Zhang, Sujata V. Ghate, Lars J. Grimm
Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis. Computer-Aided Diagnosis 2018: 105752V - [c25]Zhe Zhu, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz
Breast cancer molecular subtype classification using deep features: preliminary results. Computer-Aided Diagnosis 2018: 105752X - [c24]Zhe Zhu, Michael R. Harowicz
Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data. Computer-Aided Diagnosis 2018: 105752W - [c23]Ashirbani Saha, Michael R. Harowicz
Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation. Computer-Aided Diagnosis 2018: 1057507 - [c22]Jun Zhang, Elizabeth Hope Cain, Ashirbani Saha, Zhe Zhu, Maciej A. Mazurowski:
Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression. Computer-Aided Diagnosis 2018: 1057525 - [i5]Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir:
Deep learning in radiology: an overview of the concepts and a survey of the state of the art. CoRR abs/1802.08717 (2018) - [i4]Jun Zhang, Ashirbani Saha, Brian J. Soher, Maciej A. Mazurowski:
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images. CoRR abs/1807.02152 (2018) - 2017
- [j9]Ashirbani Saha, Xiaozhi Yu, Dushyant Sahoo
Effects of MRI scanner parameters on breast cancer radiomics. Expert Syst. Appl. 87: 384-391 (2017) - [c21]Maciej A. Mazurowski, Kal Clark, Nicholas M. Czarnek
Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics. Computer-Aided Diagnosis 2017: 101341T - [c20]David Paredes, Ashirbani Saha, Maciej A. Mazurowski:
Deep learning for segmentation of brain tumors: can we train with images from different institutions? Computer-Aided Diagnosis 2017: 101341P - [c19]Bibo Shi, Lars J. Grimm
Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features. Computer-Aided Diagnosis 2017: 101341I - [c18]Bibo Shi, Lars J. Grimm
Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features? Computer-Aided Diagnosis 2017: 101342X - [i3]Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski:
A systematic study of the class imbalance problem in convolutional neural networks. CoRR abs/1710.05381 (2017) - [i2]Zhe Zhu, Michael R. Harowicz, Jun Zhang, Ashirbani Saha, Lars J. Grimm, Eun-Sil Shelley Hwang, Maciej A. Mazurowski:
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. CoRR abs/1711.10577 (2017) - [i1]Zhe Zhu, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, Maciej A. Mazurowski:
Deep Learning for identifying radiogenomic associations in breast cancer. CoRR abs/1711.11097 (2017) - 2016
- [j8]Mengyu Wang, Jing Zhang, Lars J. Grimm
Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach. Expert Syst. Appl. 56: 1-8 (2016) - [j7]Mengyu Wang, Meng Wang, Lars J. Grimm
A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases. Expert Syst. Appl. 64: 490-499 (2016) - [c17]Nicholas M. Czarnek
Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype. Computer-Aided Diagnosis 2016: 97850V - [c16]Maciej A. Mazurowski, Nicholas M. Czarnek
Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data. Computer-Aided Diagnosis 2016: 97852T - [c15]Mengyu Wang, Jing Zhang, Lars J. Grimm
Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features. Image Perception, Observer Performance, and Technology Assessment 2016: 978709 - 2015
- [j6]Jing Zhang, James I. Silber, Maciej A. Mazurowski:
Modeling false positive error making patterns in radiology trainees for improved mammography education. J. Biomed. Informatics 54: 50-57 (2015) - 2013
- [j5]Maciej A. Mazurowski:
Estimating confidence of individual rating predictions in collaborative filtering recommender systems. Expert Syst. Appl. 40(10): 3847-3857 (2013) - 2012
- [j4]Jordan M. Malof, Maciej A. Mazurowski, Georgia D. Tourassi
The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support. Neural Networks 25: 141-145 (2012) - 2011
- [j3]Maciej A. Mazurowski, Joseph Y. Lo
Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis. J. Biomed. Informatics 44(5): 815-823 (2011) - 2010
- [c14]Georgia D. Tourassi
Perception-driven IT-CADe analysis for the detection of masses in screening mammography: initial investigation. Computer-Aided Diagnosis 2010: 762406
2000 – 2009
- 2009
- [j2]Jacek M. Zurada, Maciej A. Mazurowski, Rommohan Ragade, Artur Abdullin, Janusz Wojtusiak, James E. Gentle:
Building virtual community in computational intelligence and machine learning [Research Frontier]. IEEE Comput. Intell. Mag. 4(1): 43-54 (2009) - [c13]Jordan M. Malof, Maciej A. Mazurowski, Georgia D. Tourassi
The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems. IJCNN 2009: 1975-1980 - [c12]Maciej A. Mazurowski, Georgia D. Tourassi
Evaluating classifiers: Relation between area under the receiver operator characteristic curve and overall accuracy. IJCNN 2009: 2045-2049 - [c11]Maciej A. Mazurowski, Jordan M. Malof, Jacek M. Zurada, Georgia D. Tourassi
A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results. Computer-Aided Diagnosis 2009: 72600F - [c10]Maciej A. Mazurowski, Georgia D. Tourassi
Relational representation for improved decisions with an information-theoretic CADe system: initial experience. Computer-Aided Diagnosis 2009: 726018 - 2008
- [j1]Maciej A. Mazurowski, Piotr A. Habas, Jacek M. Zurada, Joseph Y. Lo
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Networks 21(2-3): 427-436 (2008) - [c9]Jacek M. Zurada, Janusz Wojtusiak, Fahmida Chowdhury, James E. Gentle, Cedric J. Jeannot, Maciej A. Mazurowski:
Computational intelligence virtual community: Framework and implementation issues. IJCNN 2008: 3153-3157 - [c8]Maciej A. Mazurowski, Jacek M. Zurada, Georgia D. Tourassi
Reliability Assessment of Ensemble Classifiers: Application in Mammography. Digital Mammography / IWDM 2008: 366-370 - [c7]Maciej A. Mazurowski, Jacek M. Zurada, Georgia D. Tourassi
Database decomposition of a knowledge-based CAD system in mammography: an ensemble approach to improve detection. Computer-Aided Diagnosis 2008: 69151K - 2007
- [c6]Maciej A. Mazurowski, Piotr A. Habas, Georgia D. Tourassi
Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms. IEEE Congress on Evolutionary Computation 2007: 600-605 - [c5]Maciej A. Mazurowski, Jacek M. Zurada:
Solving decentralized multi-agent control problems with genetic algorithms. IEEE Congress on Evolutionary Computation 2007: 1029-1034 - [c4]Georgia D. Tourassi
Stacked Generalization in Computer-Assisted Decision Systems: Empirical Comparison of Data Handling Schemes. IJCNN 2007: 1343-1347 - [c3]Maciej A. Mazurowski, Piotr A. Habas, Georgia D. Tourassi
Impact of Low Class Prevalence on the Performance Evaluation of Neural Network Based Classifiers: Experimental Study in the Context of Computer-Assisted Medical Diagnosis. IJCNN 2007: 2005-2009 - [c2]Maciej A. Mazurowski, Jacek M. Zurada:
Solving Multi-agent Control Problems Using Particle Swarm Optimization. SIS 2007: 105-111 - 2005
- [c1]Przemyslaw Szecówka, Andrzej Szczurek
Neural Network Sensitivity Analysis Applied for the Reduction of the Sensor Matrix. EUROCAST 2005: 27-32
Coauthor Index
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last updated on 2024-11-28 20:30 CET by the dblp team
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