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Deep learning in medical imaging and radiation therapy

Medical physics (Lancaster), 2019-01, Vol.46 (1), p.e1-e36

2019

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Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets

IEEE transactions on medical imaging, 2019-03, Vol.38 (3), p.686-696

2019

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Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

Physics in medicine & biology, 2017-11, Vol.62 (23), p.8894-8908

2017

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Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

Medical physics (Lancaster), 2016-04, Vol.43 (4), p.1882-1896

2016

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Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning

Scientific reports, 2017-08, Vol.7 (1), p.8738-12, Article 8738

2017

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U‐Net based deep learning bladder segmentation in CT urography

Medical physics (Lancaster), 2019-04, Vol.46 (4), p.1752-1765

2019

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Urinary bladder cancer staging in CT urography using machine learning

Medical physics (Lancaster), 2017-11, Vol.44 (11), p.5814-5823

2017

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Methodology for Good Machine Learning with Multi‐Omics Data

Clinical pharmacology and therapeutics, 2024-04, Vol.115 (4), p.745-757

2024

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Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning

Journal of medical imaging (Bellingham, Wash.), 2020-01, Vol.7 (1), p.012703-012703

2020

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Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support

Academic radiology, 2019-09, Vol.26 (9), p.1137-1145

2019

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Deep‐learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography

Medical physics (Lancaster), 2019-02, Vol.46 (2), p.634-648

2019

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SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment

Journal of medical imaging (Bellingham, Wash.), 2021-05, Vol.8 (3), p.034501-034501

2021

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Decision region analysis for generalizability of artificial intelligence models: estimating model generalizability in the case of cross-reactivity and population shift

Journal of medical imaging (Bellingham, Wash.), 2024-01, Vol.11 (1)

2024

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Deep Learning Approach for Assessment of Bladder Cancer Treatment Response

Tomography (Ann Arbor), 2019-03, Vol.5 (1), p.201-208

2019

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End-to-end deep learning method for predicting hormonal treatment response in women with atypical endometrial hyperplasia or endometrial cancer

Journal of medical imaging (Bellingham, Wash.), 2024-01, Vol.11 (1)

2024

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MP18-20 ASSESSMENT OF TO RESPONSE RATE FOLLOWING NEOADJUVANT CHEMOTHERAPY FOR BLADDER CANCER UTILIZING A COMPUTERIZED VOLUME ANALYSIS SYSTEM

The Journal of urology, 2017-04, Vol.197 (4), p.e228-e228

2017

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Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study

Tomography (Ann Arbor), 2016-12, Vol.2 (4), p.421-429

2016

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Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support

Tomography (Ann Arbor), 2020-06, Vol.6 (2), p.194-202

2020

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Software as a Medical Device (SaMD) at the FDA: Regulatory Science and Review

2023 IEEE John Vincent Atanasoff International Symposium on Modern Computing (JVA), 2023, p.71-74

Cha, Kenny H.

2023

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AFE-GAN: Synthesizing Electrocardiograms with Atrial Fibrillation Characteristics Using Generative Adversarial Networks

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023, Vol.2023, p.1-5

2023

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Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing

BJR artificial intelligence, 2024-03, Vol.1 (1), p.ubae003-ubae003

2024

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AI and machine learning in medical imaging: key points from development to translation

BJR artificial intelligence, 2024-01, Vol.1 (1), p.ubae006-ubae006

2024

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Coordinated regulation of acid resistance in Escherichia coli

BMC systems biology, 2017-01, Vol.11 (1), p.1-1, Article 1

2017

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CT urography: segmentation of urinary bladder using CLASS with local contour refinement

Physics in medicine & biology, 2014-06, Vol.59 (11), p.2767-2785

2014

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Deep learning in medical imaging and radiation therapy

Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning

U‐Net based deep learning bladder segmentation in CT urography

Computerized Decision Support for Bladder Cancer Treatment Response Assessment in CT Urography: Effect on Diagnostic Accuracy in Multi-Institution Multi-Specialty Study

Urinary bladder cancer staging in CT urography using machine learning

Methodology for Good Machine Learning with Multi‐Omics Data

Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning

Diagnostic Accuracy of CT for Prediction of Bladder Cancer Treatment Response with and without Computerized Decision Support

Deep‐learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography

SPIE-AAPM-NCI BreastPathQ Challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment

Decision region analysis for generalizability of artificial intelligence models: estimating model generalizability in the case of cross-reactivity and population shift

Deep Learning Approach for Assessment of Bladder Cancer Treatment Response

End-to-end deep learning method for predicting hormonal treatment response in women with atypical endometrial hyperplasia or endometrial cancer

MP18-20 ASSESSMENT OF TO RESPONSE RATE FOLLOWING NEOADJUVANT CHEMOTHERAPY FOR BLADDER CANCER UTILIZING A COMPUTERIZED VOLUME ANALYSIS SYSTEM

Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network-A Pilot Study

Intraobserver Variability in Bladder Cancer Treatment Response Assessment With and Without Computerized Decision Support

Software as a Medical Device (SaMD) at the FDA: Regulatory Science and Review

AFE-GAN: Synthesizing Electrocardiograms with Atrial Fibrillation Characteristics Using Generative Adversarial Networks

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing

AI and machine learning in medical imaging: key points from development to translation

Coordinated regulation of acid resistance in Escherichia coli

CT urography: segmentation of urinary bladder using CLASS with local contour refinement

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