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CAD and AI for breast cancer-recent development and challenges

British journal of radiology, 2020-04, Vol.93 (1108), p.20190580-20190580

2020

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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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IEEE_Xplore

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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Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

Physics in medicine & biology, 2018-05, Vol.63 (9), p.095005-095005

2018

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Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

Physics in medicine & biology, 2018-01, Vol.63 (2), p.025005-025005

2018

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Deep Learning in Medical Image Analysis

Advances in experimental medicine and biology, 2020, Vol.1213, p.3-21

2020

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Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Medical physics (Lancaster), 2015-01, Vol.42 (1), p.182-195

2015

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Computer‐aided diagnosis in the era of deep learning

Medical physics (Lancaster), 2020-06, Vol.47 (5), p.e218-e227

2020

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Data drift in medical machine learning: implications and potential remedies

British journal of radiology, 2023-10, Vol.96 (1150), p.20220878-20220878

2023

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Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Medical physics (Lancaster), 2016-12, Vol.43 (12), p.6654-6666

2016

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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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Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data

Diagnostics (Basel), 2024-02, Vol.14 (3), p.341

2024

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Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification

Medical physics (Lancaster), 2021-06, Vol.48 (6), p.2827-2837

2021

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Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis

Physics in medicine & biology, 2020-05, Vol.65 (10), p.105002-105002

2020

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Computer-aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images

Physics in medicine & biology, 2015-11, Vol.60 (21), p.8457-8479

2015

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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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Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

Physics in medicine & biology, 2014-12, Vol.59 (23), p.7457-7477

2014

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Analysis of computer-aided detection techniques and signal characteristics for clustered microcalcifications on digital mammography and digital breast tomosynthesis

Physics in medicine & biology, 2016-10, Vol.61 (19), p.7092-7112

2016

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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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Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction

Physics in medicine & biology, 2017-09, Vol.62 (19), p.7765-7783

2017

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Effect of Dose Level on Radiologists’ Detection of Microcalcifications in Digital Breast Tomosynthesis: An Observer Study with Breast Phantoms

Academic radiology, 2022-01, Vol.29, p.S42-S49

2022

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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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CAD and AI for breast cancer-recent development and challenges

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

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

Deep Learning in Medical Image Analysis

Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Computer‐aided diagnosis in the era of deep learning

Data drift in medical machine learning: implications and potential remedies

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

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

Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data

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

Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification

Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis

Computer-aided detection system for clustered microcalcifications in digital breast tomosynthesis using joint information from volumetric and planar projection images

Methodology for Good Machine Learning with Multi‐Omics Data

Digital breast tomosynthesis: computer-aided detection of clustered microcalcifications on planar projection images

Analysis of computer-aided detection techniques and signal characteristics for clustered microcalcifications on digital mammography and digital breast tomosynthesis

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

Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction

Effect of Dose Level on Radiologists’ Detection of Microcalcifications in Digital Breast Tomosynthesis: An Observer Study with Breast Phantoms

Deep Learning Approach for Assessment of Bladder Cancer Treatment Response

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