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Cancer Research|November 3, 2017
An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and ManagementAnne L Martel, Dan Hosseinzadeh, Caglar Senaras, et al.Cancers|September 24, 2020
Computer Extracted Features from Initial H&E Tissue Biopsies Predict Disease Progression for Prostate Cancer Patients on Active SurveillanceSacheth Chandramouli, Patrick Leo, George Lee, et al.Journal of Medical Imaging (Bellingham, Wash.)|July 2, 2019
Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRIPrathyush Chirra, Patrick Leo, Michael Yim, et al.European Radiology|July 24, 2020
Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) mapsAmogh Hiremath, Rakesh Shiradkar, Harri Merisaari, et al.Magnetic Resonance in Medicine|November 9, 2019
Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancerHarri Merisaari, Pekka Taimen, Rakesh Shiradkar, et al.Cancer Letters|November 14, 2025
A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology imagesXiangxue Wang, Yufei Zhou, Cristian Barrera, et al.Journal of Magnetic Resonance Imaging : JMRI|February 17, 2012
Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagerySatish E Viswanath, Nicholas B Bloch, Jonathan C Chappelow, et al.Cancers|July 2, 2021
A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT ScansMehdi Alilou, Prateek Prasanna, Kaustav Bera, et al.Nature Reviews. Drug Discovery|April 13, 2019
Applications of machine learning in drug discovery and developmentJessica Vamathevan, Dominic Clark, Paul Czodrowski, et al.Journal of Digital Imaging|May 29, 2010
Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classificationShannon C Agner, Salil Soman, Edward Libfeld, et al.Pageof 41