Literature DB >> 30422287

On Deep Learning for Medical Image Analysis.

Lawrence Carin1, Michael J Pencina2.   

Abstract

Mesh：

Year:  2018        PMID： 30422287     DOI： 10.1001/jama.2018.13316

Source DB:  PubMed          Journal:  JAMA        ISSN： 0098-7484            Impact factor:   56.272


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  16 in total

1.  Age estimation based on 3D pulp chamber segmentation of first molars from cone-beam-computed tomography by integrated deep learning and level set.

Authors:  Qiang Zheng; Zhipu Ge; Han Du; Gang Li
Journal:  Int J Legal Med       Date:  2020-11-13       Impact factor: 2.686

2.  Comprehensive enhanced methodology of an MRI-based automated left-ventricular chamber quantification algorithm and validation in chemotherapy-related cardiotoxicity.

Authors:  Julia Kar; Michael V Cohen; Samuel A McQuiston; Christopher M Malozzi
Journal:  J Med Imaging (Bellingham)       Date:  2020-11-16

3.  Validation of a deep-learning semantic segmentation approach to fully automate MRI-based left-ventricular deformation analysis in cardiotoxicity.

Authors:  Julia Karr; Michael Cohen; Samuel A McQuiston; Teja Poorsala; Christopher Malozzi
Journal:  Br J Radiol       Date:  2021-02-24       Impact factor: 3.039

4.  A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity.

Authors:  By Julia Kar; Michael V Cohen; Samuel P McQuiston; Christopher M Malozzi
Journal:  Magn Reson Imaging       Date:  2021-02-08       Impact factor: 2.546

5.  Dual-scale categorization based deep learning to evaluate programmed cell death ligand 1 expression in non-small cell lung cancer.

Authors:  Xiangyun Wang; Peilin Chen; Guangtai Ding; Yishi Xing; Rongrong Tang; Chaolong Peng; Yizhou Ye; Qiang Fu
Journal:  Medicine (Baltimore)       Date:  2021-05-21       Impact factor: 1.817

6.  Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes.

Authors:  Rahul Rajendran; Kevan Iffrig; Deepak K Pruthi; Allison Wheeler; Brian Neuman; Dharam Kaushik; Ahmed M Mansour; Karen Panetta; Sos Agaian; Michael A Liss
Journal:  Adv Urol       Date:  2019-04-23

Review 7.  The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence.

Authors:  Josef Huemer; Siegfried K Wagner; Dawn A Sim
Journal:  Clin Ophthalmol       Date:  2020-07-20

8.  Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.

Authors:  Yan Li; Matthew Sperrin; Darren M Ashcroft; Tjeerd Pieter van Staa
Journal:  BMJ       Date:  2020-11-04

9.  Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study.

Authors:  Yifei Zhang; Juan Shi; Ying Peng; Zhiyun Zhao; Qidong Zheng; Zilong Wang; Kun Liu; Shengyin Jiao; Kexin Qiu; Ziheng Zhou; Li Yan; Dong Zhao; Hongwei Jiang; Yuancheng Dai; Benli Su; Pei Gu; Heng Su; Qin Wan; Yongde Peng; Jianjun Liu; Ling Hu; Tingyu Ke; Lei Chen; Fengmei Xu; Qijuan Dong; Demetri Terzopoulos; Guang Ning; Xun Xu; Xiaowei Ding; Weiqing Wang
Journal:  BMJ Open Diabetes Res Care       Date:  2020-10

10.  Adversarial attack on deep learning-based dermatoscopic image recognition systems: Risk of misdiagnosis due to undetectable image perturbations.

Authors:  Jérôme Allyn; Nicolas Allou; Charles Vidal; Amélie Renou; Cyril Ferdynus
Journal:  Medicine (Baltimore)       Date:  2020-12-11       Impact factor: 1.817
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