Neural Network Sensitivity Analysis Applied for the Reduction of the Sensor Matrix. - Przemyslaw Szecówka; Andrzej Szczurek; Maciej A. Mazurowski; Benedykt Licznerski; Franz Pichler (EUROCAST, 2005)

Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms. - Maciej A. Mazurowski; Piotr A. Habas; Georgia D. Tourassi; Jacek M. Zurada (IEEE Congress on Evolutionary Computation, 2007)

Solving decentralized multi-agent control problems with genetic algorithms. - Maciej A. Mazurowski; Jacek M. Zurada (IEEE Congress on Evolutionary Computation, 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. - Maciej A. Mazurowski; Piotr A. Habas; Georgia D. Tourassi; Jacek M. Zurada (IJCNN, 2007)

Stacked Generalization in Computer-Assisted Decision Systems: Empirical Comparison of Data Handling Schemes. - Georgia D. Tourassi; Jonathan L. Jesneck; Maciej A. Mazurowski; Piotr A. Habas (IJCNN, 2007)

Solving Multi-agent Control Problems Using Particle Swarm Optimization. - Maciej A. Mazurowski; Jacek M. Zurada (SIS, 2007)

Computational intelligence virtual community: Framework and implementation issues. - Jacek M. Zurada; Janusz Wojtusiak; Fahmida Chowdhury; James E. Gentle; Cedric J. Jeannot; Maciej A. Mazurowski (IJCNN, 2008)

Reliability Assessment of Ensemble Classifiers: Application in Mammography. - Maciej A. Mazurowski; Jacek M. Zurada; Georgia D. Tourassi (Digital Mammography / IWDM, 2008)

Database decomposition of a knowledge-based CAD system in mammography: an ensemble approach to improve detection. - Maciej A. Mazurowski; Jacek M. Zurada; Georgia D. Tourassi (Computer-Aided Diagnosis, 2008)

Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. - Maciej A. Mazurowski; Piotr A. Habas; Jacek M. Zurada; Joseph Y. Lo; Jay A. Baker; Georgia D. Tourassi (Neural Networks, 2008)

The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems. - Jordan M. Malof; Maciej A. Mazurowski; Georgia D. Tourassi (IJCNN, 2009)

Evaluating classifiers: Relation between area under the receiver operator characteristic curve and overall accuracy. - Maciej A. Mazurowski; Georgia D. Tourassi (IJCNN, 2009)

A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results. - Maciej A. Mazurowski; Jordan M. Malof; Jacek M. Zurada; Georgia D. Tourassi (Computer-Aided Diagnosis, 2009)

Relational representation for improved decisions with an information-theoretic CADe system: initial experience. - Maciej A. Mazurowski; Georgia D. Tourassi (Computer-Aided Diagnosis, 2009)

Building virtual community in computational intelligence and machine learning [Research Frontier]. - Jacek M. Zurada; Maciej A. Mazurowski; Rommohan Ragade; Artur Abdullin; Janusz Wojtusiak; James E. Gentle (IEEE Comput. Intell. Mag., 2009)

Perception-driven IT-CADe analysis for the detection of masses in screening mammography: initial investigation. - Georgia D. Tourassi; Maciej A. Mazurowski; Elizabeth A. Krupinski (Computer-Aided Diagnosis, 2010)

Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis. - Maciej A. Mazurowski; Joseph Y. Lo; Brian P. Harrawood; Georgia D. Tourassi (J. Biomed. Informatics, 2011)

The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support. - Jordan M. Malof; Maciej A. Mazurowski; Georgia D. Tourassi (Neural Networks, 2012)

Estimating confidence of individual rating predictions in collaborative filtering recommender systems. - Maciej A. Mazurowski (Expert Syst. Appl., 2013)

Modeling false positive error making patterns in radiology trainees for improved mammography education. - Jing Zhang; James I. Silber; Maciej A. Mazurowski (J. Biomed. Informatics, 2015)

Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype. - Nicholas M. Czarnek; Kal Clark; Katherine B. Peters; Leslie M. Collins; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2016)

Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data. - Maciej A. Mazurowski; Nicholas M. Czarnek; Leslie M. Collins; Katherine B. Peters; Kal Clark (Computer-Aided Diagnosis, 2016)

Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features. - Mengyu Wang 001; Jing Zhang; Lars J. Grimm; Sujata V. Ghate; Ruth Walsh; Karen S. Johnson; Joseph Y. Lo; Maciej A. Mazurowski (Image Perception, Observer Performance, and Technology Assessment, 2016)

A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases. - Mengyu Wang 001; Meng Wang; Lars J. Grimm; Maciej A. Mazurowski (Expert Syst. Appl., 2016)

Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach. - Mengyu Wang 001; Jing Zhang; Lars J. Grimm; Sujata V. Ghate; Ruth Walsh; Karen S. Johnson; Joseph Y. Lo; Maciej A. Mazurowski (Expert Syst. Appl., 2016)

Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics. - Maciej A. Mazurowski; Kal Clark; Nicholas M. Czarnek; Parisa Shamsesfandabadi; Katherine B. Peters; Ashirbani Saha (Computer-Aided Diagnosis, 2017)

Deep learning for segmentation of brain tumors: can we train with images from different institutions? - David Paredes; Ashirbani Saha; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2017)

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features. - Bibo Shi; Lars J. Grimm; Maciej A. Mazurowski; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (Computer-Aided Diagnosis, 2017)

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features? - Bibo Shi; Lars J. Grimm; Maciej A. Mazurowski; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (Computer-Aided Diagnosis, 2017)

Effects of MRI scanner parameters on breast cancer radiomics. - Ashirbani Saha; Xiaozhi Yu; Dushyant Sahoo; Maciej A. Mazurowski (Expert Syst. Appl., 2017)

Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features. - Rui Hou 002; Bibo Shi; Lars J. Grimm; Maciej A. Mazurowski; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (Computer-Aided Diagnosis, 2018)

Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation. - Ashirbani Saha; Michael R. Harowicz; Lars J. Grimm; Connie E. Kim; Ruth Walsh; Sujata V. Ghate; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2018)

Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning. - Bibo Shi; Rui Hou 002; Maciej A. Mazurowski; Lars J. Grimm; Yinhao Ren; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (Computer-Aided Diagnosis, 2018)

Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression. - Jun Zhang 018; Elizabeth Hope Cain; Ashirbani Saha; Zhe Zhu; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2018)

Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis. - Jun Zhang 018; Sujata V. Ghate; Lars J. Grimm; Ashirbani Saha; Elizabeth Hope Cain; Zhe Zhu; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2018)

Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics. - Jun Zhang 018; Ashirbani Saha; Zhe Zhu; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2018)

Breast cancer molecular subtype classification using deep features: preliminary results. - Zhe Zhu; Ehab Albadawy; Ashirbani Saha; Jun Zhang 018; Michael R. Harowicz; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2018)

Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data. - Zhe Zhu; Michael R. Harowicz; Jun Zhang 018; Ashirbani Saha; Lars J. Grimm; Eun-Sil Shelley Hwang; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2018)

Deep learning in radiology: an overview of the concepts and a survey of the state of the art. - Maciej A. Mazurowski; Mateusz Buda; Ashirbani Saha; Mustafa R. Bashir (CoRR, 2018)

Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images. - Jun Zhang 018; Ashirbani Saha; Brian J. Soher; Maciej A. Mazurowski (CoRR, 2018)

A systematic study of the class imbalance problem in convolutional neural networks. - Mateusz Buda; Atsuto Maki; Maciej A. Mazurowski (Neural Networks, 2018)

Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports. - Matias Benitez; James Tian; Mark Kelly; Vignesh Selvakumaran; Matthew Phelan; Maciej A. Mazurowski; Joseph Y. Lo; Geoffrey D. Rubin; Ricardo Henao (Computer-Aided Diagnosis, 2019)

Malignant microcalcification clusters detection using unsupervised deep autoencoders. - Rui Hou 002; Yinhao Ren; Lars J. Grimm; Maciej A. Mazurowski; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (Computer-Aided Diagnosis, 2019)

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. - Mateusz Buda; Ashirbani Saha; Maciej A. Mazurowski (Comput. Biol. Medicine, 2019)

Deep learning for identifying radiogenomic associations in breast cancer. - Zhe Zhu; Ehab Albadawy; Ashirbani Saha; Jun Zhang 018; Michael R. Harowicz; Maciej A. Mazurowski (Comput. Biol. Medicine, 2019)

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. - Zhe Zhu; Michael R. Harowicz; Jun Zhang 018; Ashirbani Saha; Lars J. Grimm; Eun-Sil Shelley Hwang; Maciej A. Mazurowski (Comput. Biol. Medicine, 2019)

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics. - Jun Zhang 018; Ashirbani Saha; Zhe Zhu; Maciej A. Mazurowski (IEEE Trans. Medical Imaging, 2019)

A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography. - Rui Hou 002; Lars J. Grimm; Maciej A. Mazurowski; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (Computer-Aided Diagnosis, 2020)

Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms. - Nianyi Li; Albert Swiecicki; Nicholas Said; Jonathan O'Donnell; William A. Jiranek; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2020)

MRI image harmonization using cycle-consistent generative adversarial network. - Gourav Modanwal; Adithya Vellal; Mateusz Buda; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2020)

Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. - Anindo Saha; Fakrul Islam Tushar; Khrystyna Faryna; Vincent M. D'Anniballe; Rui Hou 002; Maciej A. Mazurowski; Geoffrey D. Rubin; Joseph Y. Lo (Computer-Aided Diagnosis, 2020)

Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images. - Albert Swiecicki; Mateusz Buda; Ashirbani Saha; Nianyi Li; Sujata V. Ghate; Ruth Walsh; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2020)

Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms. - Albert Swiecicki; Nicholas Said; Jonathan O'Donnell; Mateusz Buda; Nianyi Li; William A. Jiranek; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2020)

Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans. - Fakrul Islam Tushar; Vincent M. D'Anniballe; Rui Hou 002; Maciej A. Mazurowski; Wanyi Fu; Ehsan Samei; Geoffrey D. Rubin; Joseph Y. Lo (CoRR, 2020)

Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5, 060 patients and a deep learning model. - Mateusz Buda; Ashirbani Saha; Ruth Walsh; Sujata V. Ghate; Nianyi Li; Albert Swiecicki; Joseph Y. Lo; Maciej A. Mazurowski (CoRR, 2020)

Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation. - Rui Hou 002; Maciej A. Mazurowski; Lars J. Grimm; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (IEEE Trans. Biomed. Eng., 2020)

Deep neural networks trained for segmentation are sensitive to brightness changes: preliminary results. - Zhe Zhu; Mustafa R. Bashir; Maciej A. Mazurowski (Computer-Aided Diagnosis, 2021)

Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. - Albert Swiecicki; Nianyi Li; Jonathan O'Donnell; Nicholas Said; Jichen Yang; Richard C. Mather; William A. Jiranek; Maciej A. Mazurowski (Comput. Biol. Medicine, 2021)

Normalization of breast MRIs using cycle-consistent generative adversarial networks. - Gourav Modanwal; Adithya Vellal; Maciej A. Mazurowski (Comput. Methods Programs Biomed., 2021)

Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning. - Vincent M. D'Anniballe; Fakrul Islam Tushar; Khrystyna Faryna; Songyue Han; Maciej A. Mazurowski; Geoffrey D. Rubin; Joseph Y. Lo (CoRR, 2021)

Do We Expect More from Radiology AI than from Radiologists? - Maciej A. Mazurowski (CoRR, 2021)

REPLICA: Enhanced Feature Pyramid Network by Local Image Translation and Conjunct Attention for High-Resolution Breast Tumor Detection. - Yifan Zhang; Haoyu Dong 003; Nicholas Konz; Hanxue Gu; Maciej A. Mazurowski (CoRR, 2021)

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. - Rachel Lea Draelos; David Dov; Maciej A. Mazurowski; Joseph Y. Lo; Ricardo Henao; Geoffrey D. Rubin; Lawrence Carin (Medical Image Anal., 2021)

Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography. - Fakrul Islam Tushar; Ehsan Abadi; Saman Sotoudeh-Paima; Rafael B. Fricks; Maciej A. Mazurowski; William Paul Segars; Ehsan Samei; Joseph Y. Lo (Computer-Aided Diagnosis, 2022)

The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning. - Nicholas Konz; Hanxue Gu; Haoyu Dong 003; Maciej A. Mazurowski (MICCAI (8), 2022)

Lightweight Transformer Backbone for Medical Object Detection. - Yifan Zhang; Haoyu Dong 003; Nicholas Konz; Hanxue Gu; Maciej A. Mazurowski (CaPTion@MICCAI, 2022)

Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT. - Fakrul Islam Tushar; Husam Nujaim; Wanyi Fu; Ehsan Abadi; Maciej A. Mazurowski; Ehsan Samei; William Paul Segars; Joseph Y. Lo (CoRR, 2022)

Automated Grading of Radiographic Knee Osteoarthritis Severity Combined with Joint Space Narrowing. - Hanxue Gu; Keyu Li; Roy J. Colglazier; Jichen Yang; Michael Lebhar; Jonathan O'Donnell; William A. Jiranek; Richard C. Mather; Rob J. French; Nicholas Said; Jikai Zhang; Christine Park; et al. (CoRR, 2022)

Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset. - 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 (CoRR, 2022)

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. - Vincent M. D'Anniballe; Fakrul Islam Tushar; Khrystyna Faryna; Songyue Han; Maciej A. Mazurowski; Geoffrey D. Rubin; Joseph Y. Lo (BMC Medical Informatics Decis. Mak., 2022)

3D Pyramid Pooling Network for Abdominal MRI Series Classification. - Zhe Zhu; Amber Mittendorf; Erin Shropshire; Brian C. Allen; Chad M. Miller; Mustafa R. Bashir; Maciej A. Mazurowski (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Anomaly Detection of Calcifications in Mammography Based on 11, 000 Negative Cases. - Rui Hou 002; Yifan Peng 004; Lars J. Grimm; Yinhao Ren; Maciej A. Mazurowski; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; Eun-Sil Shelley Hwang; Joseph Y. Lo (IEEE Trans. Biomed. Eng., 2022)

SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs. - Hanxue Gu; Hongyu He; Roy J. Colglazier; Jordan Axelrod; Robert French; Maciej A. Mazurowski (MIDL, 2023)

Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images. - Nicholas Konz; Maciej A. Mazurowski (MIDL, 2023)

Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation. - Jikai Zhang; Maciej A. Mazurowski; Brian C. Allen; Benjamin Wildman-Tobriner (Artif. Intell. Medicine, 2023)

A systematic study of the foreground-background imbalance problem in deep learning for object detection. - Hanxue Gu; Haoyu Dong 003; Nicholas Konz; Maciej A. Mazurowski (CoRR, 2023)

Domain Generalization for Medical Image Analysis: A Survey. - Jee Seok Yoon; Kwanseok Oh; Yooseung Shin; Maciej A. Mazurowski; Heung-Il Suk (CoRR, 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. - Yixin Zhang; Shen Zhao; Hanxue Gu; Maciej A. Mazurowski (CoRR, 2023)

Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion. - Nicholas Konz; Haoyu Dong 003; Maciej A. Mazurowski (Medical Image Anal., 2023)

Deep Learning for Breast MRI Style Transfer with Limited Training Data. - Shixing Cao; Nicholas Konz; James S. Duncan; Maciej A. Mazurowski (J. Digit. Imaging, 2023)

Segment anything model for medical image analysis: An experimental study. - Maciej A. Mazurowski; Haoyu Dong 003; Hanxue Gu; Jichen Yang; Nicholas Konz; Yixin Zhang (Medical Image Anal., 2023)

Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach. - Jikai Zhang; Carlos Santos; Christine Park; Maciej A. Mazurowski; Roy J. Colglazier (J. Digit. Imaging, 2023)

SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images. - Haoyu Dong 003; Yifan Zhang; Hanxue Gu; Nicholas Konz; Yixin Zhang; Maciej A. Mazurowski (IEEE Trans. Medical Imaging, 2023)

Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation. - Haoyu Dong 003; Nicholas Konz; Hanxue Gu; Maciej A. Mazurowski (CVPR Workshops, 2024)

The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images. - Nicholas Konz; Maciej A. Mazurowski (ICLR, 2024)

Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models. - Nicholas Konz; Yuwen Chen 003; Haoyu Dong 003; Maciej A. Mazurowski (MICCAI (7), 2024)

Predicting 3D forearm fracture angle from biplanar Xray images with rotational bone pose estimation. - Hanxue Gu; Roy J. Colglazier; Jikai Zhang; Robert Lark; Benjamin Alman; Maciej A. Mazurowski (MIDL, 2024)

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? - Pedro R. A. S. Bassi; Wenxuan Li; Yucheng Tang; Fabian Isensee; Zifu Wang; Jieneng Chen; Yu-Cheng Chou; Yannick Kirchhoff; Maximilian Rokuss; Ziyan Huang; Jin Ye 002; Junjun He; et al. (NeurIPS, 2024)

ContourDiff: Unpaired Image Translation with Contour-Guided Diffusion Models. - Yuwen Chen 003; Nicholas Konz; Hanxue Gu; Haoyu Dong 003; Yaqian Chen; Lin Li; Jisoo Lee; Maciej A. Mazurowski (CoRR, 2024)

Deep learning automates Cobb angle measurement compared with multi-expert observers. - Keyu Li; Hanxue Gu; Roy J. Colglazier; Robert Lark; Elizabeth Hubbard; Robert French; Denise Smith; Jikai Zhang; Erin McCrum; Anthony Catanzano; Joseph Y. Cao; Leah Waldman; et al. (CoRR, 2024)

Rethinking Perceptual Metrics for Medical Image Translation. - Nicholas Konz; Yuwen Chen 003; Hanxue Gu; Haoyu Dong 003; Maciej A. Mazurowski (CoRR, 2024)

How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model. - Hanxue Gu; Haoyu Dong 003; Jichen Yang; Maciej A. Mazurowski (CoRR, 2024)

Segment anything model 2: an application to 2D and 3D medical images. - Haoyu Dong 003; Hanxue Gu; Yaqian Chen; Jichen Yang; Yuwen Chen 003; Maciej A. Mazurowski (CoRR, 2024)

Pre-processing and Compression: Understanding Hidden Representation Refinement Across Imaging Domains via Intrinsic Dimension. - Nicholas Konz; Maciej A. Mazurowski (CoRR, 2024)

SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images. - Zafer Yildiz; Yuwen Chen 003; Maciej A. Mazurowski (CoRR, 2024)

The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study. - Gregory Szumel; Brian Guo; Darui Lu; Rongze Gui; Tingyu Wang; Nicholas Konz; Maciej A. Mazurowski (CoRR, 2024)

Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports. - Mohamed Sobhi Jabal; Pranav I. Warman; Jikai Zhang; Kartikeye Gupta; Ayush Jain; Maciej A. Mazurowski; Walter F. Wiggins; Kirti Magudia; Evan Calabrese (CoRR, 2024)

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? - Pedro R. A. S. Bassi; Wenxuan Li; Yucheng Tang; Fabian Isensee; Zifu Wang; Jieneng Chen; Yu-Cheng Chou; Yannick Kirchhoff; Maximilian Rokuss; Ziyan Huang; Jin Ye 002; Junjun He; et al. (CoRR, 2024)

RaD: A Metric for Medical Image Distribution Comparison in Out-of-Domain Detection and Other Applications. - Nicholas Konz; Yuwen Chen 003; Hanxue Gu; Haoyu Dong 003; Yaqian Chen; Maciej A. Mazurowski (CoRR, 2024)

Quantifying the Limits of Segment Anything Model: Analyzing Challenges in Segmenting Tree-Like and Low-Contrast Structures. - Yixin Zhang; Nicholas Konz; Kevin Kramer; Maciej A. Mazurowski (CoRR, 2024)

How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images? - Yixin Zhang; Kevin Kramer; Maciej A. Mazurowski (CoRR, 2024)

Convolutional neural networks rarely learn shape for semantic segmentation. - Yixin Zhang; Maciej A. Mazurowski (Pattern Recognit., 2024)

Domain Generalization for Medical Image Analysis: A Review. - Jee Seok Yoon; Kwanseok Oh; Yooseung Shin; Maciej A. Mazurowski; Heung-Il Suk (Proc. IEEE, 2024)

SegmentAnyBone: A universal model that segments any bone at any location on MRI. - Hanxue Gu; Roy J. Colglazier; Haoyu Dong 003; Jikai Zhang; Yaqian Chen; Zafer Yildiz; Yuwen Chen 003; Lin Li; Jichen Yang; Jay Willhite; Alex M. Meyer; Brian Guo; et al. (Medical Image Anal., 2025)

Automated Muscle and Fat Segmentation in Computed Tomography for Comprehensive Body Composition Analysis. - Yaqian Chen; Hanxue Gu; Yuwen Chen 003; Jicheng Yang; Haoyu Dong 003; Joseph Y. Cao; Adrian Camarena; Christopher Mantyh; Roy J. Colglazier; Maciej A. Mazurowski (CoRR, 2025)

Breast density in MRI: an AI-based quantification and relationship to assessment in mammography. - Yaqian Chen; Lin Li; Hanxue Gu; Haoyu Dong 003; Derek L. Nguyen; Allan D. Kirk; Maciej A. Mazurowski; Eun-Sil Shelley Hwang (CoRR, 2025)

Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2. - Yuwen Chen 003; Zafer Yildiz; Qihang Li; Yaqian Chen; Haoyu Dong 003; Hanxue Gu; Nicholas Konz; Maciej A. Mazurowski (CoRR, 2025)

GuidedMorph: Two-Stage Deformable Registration for Breast MRI. - Yaqian Chen; Hanxue Gu; Haoyu Dong 003; Qihang Li; Yuwen Chen 003; Nicholas Konz; Lin Li; Maciej A. Mazurowski (CoRR, 2025)

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery. - Hanxue Gu; Yaqian Chen; Jisoo Lee; Diego Schaps; Regina Woody; Roy J. Colglazier; Maciej A. Mazurowski; Christopher Mantyh (CoRR, 2025)

MRI-CORE: A Foundation Model for Magnetic Resonance Imaging. - Haoyu Dong 003; Yuwen Chen 003; Hanxue Gu; Nicholas Konz; Yaqian Chen; Qihang Li; Maciej A. Mazurowski (CoRR, 2025)

SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI. - Roy J. Colglazier; Jisoo Lee; Haoyu Dong 003; Hanxue Gu; Yaqian Chen; Joseph Y. Cao; Zafer Yildiz; Zhonghao Liu; Nicholas Konz; Jichen Yang; Jikai Zhang; Yuwen Chen 003; et al. (CoRR, 2025)

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration? - Hanxue Gu; Yaqian Chen; Nicholas Konz; Qihang Li; Maciej A. Mazurowski (CoRR, 2025)

BreastSegNet: Multi-label Segmentation of Breast MRI. - Qihang Li; Jichen Yang; Yaqian Chen; Yuwen Chen 003; Hanxue Gu; Lars J. Grimm; Maciej A. Mazurowski (CoRR, 2025)