Clinico-Radiological Characteristic-Based Machine Learning in Reducing Unnecessary Prostate Biopsies of PI-RADS 3 Lesions with Dual Validation

2019 ◽  
Author(s):  
YanSheng Kan ◽  
Qing Zhang ◽  
Jiange Hao ◽  
Wei Wang ◽  
Junlong Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prostate Biopsies ◽  
Radiological Characteristic

Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation

2020 ◽  
Vol 30 (11) ◽  
pp. 6274-6284 ◽  
Author(s):  
Yansheng Kan ◽  
Qing Zhang ◽  
Jiange Hao ◽  
Wei Wang ◽  
Junlong Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prostate Biopsies ◽  
Radiological Characteristic

scholarly journals MP23-11 PREDICTION OF ADVERSE OUTCOMES AT RADICAL PROSTATECTOMY IN GRADE GROUP 2 AND 3 PROSTATE BIOPSIES USING MACHINE LEARNING

2020 ◽  
Vol 203 ◽  
pp. e342
Author(s):  
Nathan J Paulson* ◽  
Tal Zeevi ◽  
Maria Papademetris ◽  
John A Onofrey ◽  
Preston C Sprenkle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radical Prostatectomy ◽  
Adverse Outcomes ◽  
Grade Group ◽  
Prostate Biopsies ◽  
Group 2

scholarly journals A Hybrid Human–Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy

2021 ◽  
Author(s):  
David Dov ◽  
Serge Assaad ◽  
Ameer Syedibrahim ◽  
Jonathan Bell ◽  
Jiaoti Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Sensitivity ◽  
Learning System ◽  
Learning Approach ◽  
Learning Approaches ◽  
Prostate Biopsies ◽  
Machine Learning Approach ◽  
Benign Prostate ◽  
Common Malignancy

Context.— Prostate cancer is a common malignancy, and accurate diagnosis typically requires histologic review of multiple prostate core biopsies per patient. As pathology volumes and complexity increase, new tools to improve the efficiency of everyday practice are keenly needed. Deep learning has shown promise in pathology diagnostics, but most studies silo the efforts of pathologists from the application of deep learning algorithms. Very few hybrid pathologist–deep learning approaches have been explored, and these typically require complete review of histologic slides by both the pathologist and the deep learning system. Objective.— To develop a novel and efficient hybrid human–machine learning approach to screen prostate biopsies. Design.— We developed an algorithm to determine the 20 regions of interest with the highest probability of malignancy for each prostate biopsy; presenting these regions to a pathologist for manual screening limited the initial review by a pathologist to approximately 2% of the tissue area of each sample. We evaluated this approach by using 100 biopsies (29 malignant, 60 benign, 11 other) that were reviewed by 4 pathologists (3 urologic pathologists, 1 general pathologist) using a custom-designed graphical user interface. Results.— Malignant biopsies were correctly identified as needing comprehensive review with high sensitivity (mean, 99.2% among all pathologists); conversely, most benign prostate biopsies (mean, 72.1%) were correctly identified as needing no further review. Conclusions.— This novel hybrid system has the potential to efficiently triage out most benign prostate core biopsies, conserving time for the pathologist to dedicate to detailed evaluation of malignant biopsies.

scholarly journals PD11-01 AUTOMATED HISTOPATHOLOGIC DIAGNOSIS AND GLEASON GRADING OF PROSTATE BIOPSIES WITH MACHINE LEARNING

2019 ◽  
Vol 201 (Supplement 4) ◽  
Author(s):  
Ohad Kott* ◽  
Drew Linsley ◽  
Ali Amin ◽  
Andreas Karagounis ◽  
Dragan Golijanin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gleason Grading ◽  
Prostate Biopsies ◽  
Histopathologic Diagnosis

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

1687: Directed Prostate Biopsies Utilizing Microflow Imaging During Microbubble Contrast-Enhanced Ultrasound

2007 ◽  
Vol 177 (4S) ◽  
pp. 560-560
Author(s):  
Robert A. Linden ◽  
Paul R. Gittens ◽  
Flemming Forsberg ◽  
Edouard J. Trabulsi ◽  
Leonard G. Gomella ◽  
...  
Keyword(s):  
Contrast Enhanced Ultrasound ◽  
Prostate Biopsies ◽  
Contrast Enhanced

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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