Joseph Y. Lo
Orcid: 0000-0002-9540-5072Affiliations:
- Duke University, Durham, NC, USA
According to our database1,
Joseph Y. Lo
authored at least 92 papers
between 1996 and 2024.
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Bibliography
2024
What limits performance of weakly supervised deep learning for chest CT classification?
CoRR, 2024
2023
IEEE Trans. Medical Imaging, October, 2023
Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation.
CoRR, 2023
Data diversity and virtual imaging in AI-based diagnosis: A case study based on COVID-19.
CoRR, 2023
A user interface to communicate interpretable AI decisions to radiologists (Erratum).
Proceedings of the Medical Imaging 2023: Image Perception, 2023
Proceedings of the Medical Imaging 2023: Image Perception, 2023
Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, 2023
2022
Corrections to "iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and its Application to CT Organ Dosimetry".
IEEE J. Biomed. Health Informatics, 2022
IEEE Trans. Biomed. Eng., 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
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT.
CoRR, 2022
Interpretable deep learning models for better clinician-AI communication in clinical mammography.
Proceedings of the Medical Imaging 2022: Image Perception, 2022
Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 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
iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry.
IEEE J. Biomed. Health Informatics, 2021
A case-based interpretable deep learning model for classification of mass lesions in digital mammography.
Nat. Mach. Intell., 2021
Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.
Medical Image Anal., 2021
Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning.
CoRR, 2021
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography.
CoRR, 2021
Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning.
CoRR, 2021
A new method to accurately identify single nucleotide variants using small FFPE breast samples.
Briefings Bioinform., 2021
Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 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
Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
Attention-guided classification of abnormalities in semi-structured computed tomography reports.
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
CoRR, 2019
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Malignant microcalcification clusters detection using unsupervised deep autoencoders.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
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
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
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
Method for task-based evaluation of clinical FFDM and DBT systems using an anthropomorphic breast phantom.
Proceedings of the 14th International Workshop on Breast Imaging, 2018
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
2016
Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach.
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
2014
Development and Application of a Suite of 4-D Virtual Breast Phantoms for Optimization and Evaluation of Breast Imaging Systems.
IEEE Trans. Medical Imaging, 2014
2012
Application of a Dynamic 4D Anthropomorphic Breast Phantom in Contrast-Based Imaging System Optimization: Dual-Energy or Temporal Subtraction?
Proceedings of the Breast Imaging, 2012
2011
Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis.
J. Biomed. Informatics, 2011
2010
2008
Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance.
Neural Networks, 2008
Impulse response and Modulation Transfer Function analysis for Shift-And-Add and Back Projection image reconstruction algorithms in Digital Breast Tomosynthesis (DBT).
Int. J. Funct. Informatics Pers. Medicine, 2008
Computer-aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008
Optimized acquisition scheme for multi-projection correlation imaging of breast cancer.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008
Knowledge Transfer across Breast Cancer Screening Modalities: A Pilot Study Using an Information-Theoretic CADe System for Mass Detection.
Proceedings of the Digital Mammography, 2008
Effect of Similarity Metrics and ROI Sizes in Featureless Computer Aided Detection of Breast Masses in Tomosynthesis.
Proceedings of the Digital Mammography, 2008
Assessment of Low Energies and Slice Depth in the Quantification of Breast Tomosynthesis.
Proceedings of the Digital Mammography, 2008
Multi-projection Correlation Imaging as a New Diagnostic Tool for Improved Breast Cancer Detection.
Proceedings of the Digital Mammography, 2008
Proceedings of the International Conference on Image Processing, 2008
Mass detectability in dedicated breast CT: A simulation study with the application of volume noise removal.
Proceedings of the 8th IEEE International Conference on Bioinformatics and Bioengineering, 2008
2007
Incorporation of a Laguerre-Gauss Channelized Hotelling Observer for False-Positive Reduction in a Mammographic Mass CAD System.
J. Digit. Imaging, 2007
Breast mass detection in tomosynthesis projection images using information-theoretic similarity measures.
Proceedings of the Medical Imaging 2007: Computer-Aided Diagnosis, 2007
Proceedings of the Medical Imaging 2007: Computer-Aided Diagnosis, 2007
Proceedings of the Medical Imaging 2007: Computer-Aided Diagnosis, 2007
Decision Fusion of Circulating Markers for Breast Cancer Detection in Premenopausal Women.
Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 2007
A comparison between traditional shift-and-add (SAA) and point-by-point back projection (BP) -- relevance to morphology of microcalcifications for isocentric motion in Digital Breast tomosynthesis (DBT).
Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 2007
2006
Proceedings of the Digital Mammography, 2006
2005
Proceedings of the Medical Imaging 2005: Image Processing, 2005
2004
New results in computer-aided diagnosis (CAD) of breast cancer using a recently developed SVM/GRNN Oracle hybrid.
Proceedings of the Medical Imaging 2004: Image Processing, 2004
Breast cancer classification improvements using a new kernel function with evolutionary-programming-configured support vector machines.
Proceedings of the Medical Imaging 2004: Image Processing, 2004
2003
Artif. Intell. Medicine, 2003
Validation of a constraint satisfaction neural network for breast cancer disgnosis: new results from 1030 cases.
Proceedings of the Medical Imaging 2003: Image Processing, 2003
Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists.
Proceedings of the Medical Imaging 2003: Image Processing, 2003
Application of support vector machines to breast cancer screening using mammogram and clinical history data.
Proceedings of the Medical Imaging 2003: Image Processing, 2003
Improving the predictive value of mammography using a specialized evolutionary programming hybrid and fitness functions.
Proceedings of the Medical Imaging 2003: Image Processing, 2003
Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers.
Proceedings of the Medical Imaging 2003: Image Processing, 2003
2002
Comput. Biol. Medicine, 2002
Proceedings of the Medical Imaging 2002: Image Processing, 2002
Application of support vector machines to breast cancer screening using mammogram and history data.
Proceedings of the Medical Imaging 2002: Image Processing, 2002
Performance tradeoff between evolutionary computation (EC)/adaptive boosting (AB) hybrid and support vector machine breast cancer classification paradigms.
Proceedings of the 2002 Congress on Evolutionary Computation, 2002
2001
Application of adaptive boosting to EP-derived multilayer feed-forward neural networks (MLFN) to improve benign/malignant breast cancer classification.
Proceedings of the Medical Imaging 2001: Image Processing, 2001
Application of evolutionary computation and neural network hybrids for breast cancer classification using mammogram and history data.
Proceedings of the 2001 Congress on Evolutionary Computation, 2001
2000
Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation.
Proceedings of the Medical Imaging 2000: Image Processing, 2000
Evolutionary programming technique for reducing complexity of artifical neural networks for breast cancer diagnosis.
Proceedings of the Medical Imaging 2000: Image Processing, 2000
Application of a GRNN oracle to the intelligent combination of several breast cancer benign/malignant predictive paradigms.
Proceedings of the Medical Imaging 2000: Image Processing, 2000
Application of a new evolutionary programming/adaptive boosting hybrid to breast cancer diagnosis.
Proceedings of the 2000 Congress on Evolutionary Computation, 2000
1999
Proceedings of the Medical Imaging 1999: Image Processing, 1999
Proceedings of the International Joint Conference Neural Networks, 1999
Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis.
Proceedings of the International Joint Conference Neural Networks, 1999
Proceedings of the 1999 Congress on Evolutionary Computation, 1999
1997
Proceedings of International Conference on Neural Networks (ICNN'97), 1997
1996
Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features.
Proceedings of the Medical Imaging 1996: Image Processing, 1996