PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images-Reference-Cited by-同舟云学术

PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images

Published:2018-11-10 Issue:04 Volume:5 Page:1
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:
Short-container-title:J. Med. Imag.

Author:

Armato Samuel G.¹,Huisman Henkjan²,Drukker Karen¹,Hadjiiski Lubomir³,Kirby Justin S.⁴,Petrick Nicholas⁵,Redmond George⁶,Giger Maryellen L.¹,Cha Kenny³,Mamonov Artem⁷,Kalpathy-Cramer Jayashree⁷,Farahani Keyvan⁶

Affiliation:

1. The University of Chicago, Department of Radiology, Chicago, Illinois

2. Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen

3. University of Michigan, Department of Radiology, Ann Arbor, Michigan

4. Frederick National Laboratory for Cancer Research, Cancer Imaging Program, Frederick, Maryland

5. U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryla

6. National Cancer Institute, Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, Bethe

7. MGH/Harvard Medical School, Boston, Massachusetts

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology, Nuclear Medicine and imaging

Reference45 articles.

1. Effect of case selection on the performance of computer-aided detection schemes

2. Variations in measured performance of CAD schemes due to database composition and scoring protocol

3. The Effect of Verification on the Assessment of Imaging Techniques

4. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge

5. Designing image segmentation studies: Statistical power, sample size and reference standard quality

Cited by 118 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Part I: prostate cancer detection, artificial intelligence for prostate cancer and how we measure diagnostic performance: a comprehensive review;Current Problems in Diagnostic Radiology;2024-09

2. Addressing intra‐ and inter‐institution variability of a radiomic framework based on Apparent Diffusion Coefficient in prostate cancer;Medical Physics;2024-08-22

3. ResQu-Net: Effective prostate’s peripheral zone segmentation leveraging the representational power of attention-based mechanisms;Biomedical Signal Processing and Control;2024-07

4. ProLesA-Net: A multi-channel 3D architecture for prostate MRI lesion segmentation with multi-scale channel and spatial attentions;Patterns;2024-07

5. A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound;BMC Medical Imaging;2024-04-06