The Need for Careful Data Collection for Pattern Recognition in Digital Pathology

Fuchs, 2011, Computational pathology: Challenges and promises for tissue analysis, Comput Med Imaging Graph, 35, 515, 10.1016/j.compmedimag.2011.02.006 Kothari, 2013, Pathology imaging informatics for quantitative analysis of whole-slide images, J Am Med Inform Assoc, 20, 1099, 10.1136/amiajnl-2012-001540 McCann, 2014, Automated Histology Analysis: Opportunities for Signal Processing, IEEE Signal Processing Ponce, 2006 Herve, 2007, Image Annotation: Which Approach for Realistic Databases?, 170 Shamir, 2008, Evaluation of face datasets as tools for assessing the performance of face recognition methods, Int J Comput Vis, 79, 225, 10.1007/s11263-008-0143-7 Kumar, 2009, Attribute and Simile Classifiers for Face Verification Cox, 2011, Comparing State-of-the-Art Visual Features on Invariant Object Recognition Tasks Torralba, 2011, Unbiased Look at Dataset Bias Shamir, 2011, Assessing the efficacy of low-level image content descriptors for computer-based fluorescence microscopy image analysis, J Microsc, 243, 284, 10.1111/j.1365-2818.2011.03502.x Model, 2015, Comparison of dataset bias in object recognition benchmarks, IEEE Access, 3, 1953, 10.1109/ACCESS.2015.2491921 Harder, 2015, Large-scale tracking and classification for automatic analysis of cell migration and proliferation, and experimental optimization of high-throughput screens of neuroblastoma cells, Cytometry A, 87, 524, 10.1002/cyto.a.22632 Lezoray, 2003, A color object recognition scheme: Application to cellular sorting, Mach Vis Appl, 14, 166, 10.1007/s00138-002-0120-z Marinakis, 2009, Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification, Comput Biol Med, 39, 69, 10.1016/j.compbiomed.2008.11.006 Bengtsson, 2014, Screening for cervical cancer using automated analysis of PAP-smears, Comput Math Methods Med, 2014, 10.1155/2014/842037 Bejnordi, 2014, Quantitative Analysis of Stain Variability in Histology Slides and an Algorithm for Standardization Sahay, 2013, Cytological artifacts masquerading interpretation, J Cytol, 30, 241, 10.4103/0970-9371.126649 Mcinnes, 2005, Artefacts in histology, Comp Clin Pathol, 13, 100, 10.1007/s00580-004-0532-4 Marien, 2016 Delga, 2014, Evaluation of CellSolutions BestPrep® automated thin-layer liquid-based cytology Papanicolaou slide preparation and BestCyte® cell sorter imaging system, Acta Cytol, 58, 469, 10.1159/000367837 Giusti, 2014, A Comparison of Algorithms and Humans for Mitosis Detection Kozubek, 2016, Challenges and benchmarks in bioimage analysis, Adv Anat Embryol Cell Biol, 219, 231, 10.1007/978-3-319-28549-8_9 Shamir, 2010, Pattern recognition software and techniques for biological image analysis, PLoS Comput Biol, 6, 10.1371/journal.pcbi.1000974 Bradley, 1997, The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognit, 30, 1145, 10.1016/S0031-3203(96)00142-2 Marée, 2016, Collaborative analysis of multi-gigapixel imaging data using Cytomine, Bioinformatics, 32, 1395, 10.1093/bioinformatics/btw013 Jeanray, 2015, Phenotype classification of zebrafish embryos by supervised learning, PLoS One, 10, 10.1371/journal.pone.0116989 Krig, 2016, Ground truth data, content, metrics, and analysis Quionero-Candela, 2008 Vittayakorn, 2011, Quality Assessment for Crowdsourced Object Annotations, 109.1 Shamir, 2008, Iicbu 2008 – A Benchmark Suite for Biological Imaging Marée, 2016, Towards generic image classification using tree-based learning: An extensive empirical study, Pattern Recognit Lett, 74, 17, 10.1016/j.patrec.2016.01.006 Swamidoss, 2013, Automated classification of immunostaining patterns in breast tissue from the human protein atlas, J Pathol Inform, S14, 10.4103/2153-3539.109881 Donida Labati, 2011, All-idb: The Acute Lymphoblastic Leukemia Image Database for Image Processing Janowczyk, 2016, Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases, J Pathol Inform, 7, 29, 10.4103/2153-3539.186902 Goodfellow, 2016