Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology
- 25 September 2017
- journal article
- research article
- Published by Elsevier in Computerized Medical Imaging and Graphics
- Vol. 65, 142-151
Abstract
No abstract availableKeywords
Funding Information
- NIH/NCI Cancer Center Support (P30 CA008748)
This publication has 16 references indexed in Scilit:
- Whole slide imaging in pathology: advantages, limitations, and emerging perspectivesPathology and Laboratory Medicine International, 2015
- An Automated Blur Detection Method for Histological Whole Slide ImagingPLOS ONE, 2013
- Semantic Focusing Allows Fully Automated Single-Layer Slide Scanning of Cervical Cytology SlidesPLOS ONE, 2013
- Digital Imaging in Pathology: Whole-Slide Imaging and BeyondAnnual Review of Pathology: Mechanisms of Disease, 2013
- Image Sharpness Measure for Blurred Images in Frequency DomainProcedia Engineering, 2013
- OpenSlide: A vendor-neutral software foundation for digital pathologyJournal of Pathology Informatics, 2013
- Computational pathology: Challenges and promises for tissue analysisComputerized Medical Imaging and Graphics, 2011
- Image quality assessment: from error visibility to structural similarityIEEE Transactions on Image Processing, 2004
- Shape Quantization and Recognition with Randomized TreesNeural Computation, 1997
- Comparison of autofocus methods for automated microscopyCytometry, 1991