Digital pathology and artificial intelligence

Tizhoosh, 2018, Artificial intelligence and digital pathology: challenges and opportunities, J Pathol Inform, 9, 38, 10.4103/jpi.jpi_53_18 Farahani, 2015, Whole slide imaging in pathology: advantages, limitations, and emerging perspectives, Pathol Lab Med Int, 7, 23 Zarella, 2019, A practical guide to whole slide imaging: a white paper from the digital pathology association, Arch Pathol Lab Med, 143, 222, 10.5858/arpa.2018-0343-RA Niazi, 2018, Nuclear IHC enumeration: a digital phantom to evaluate the performance of automated algorithms in digital pathology, PloS One, 13, e0196547, 10.1371/journal.pone.0196547 Mirza Tabata, 2017, Whole-slide imaging at primary pathological diagnosis: validation of whole-slide imaging-based primary pathological diagnosis at twelve Japanese academic institutes, Pathol Int, 67, 547, 10.1111/pin.12590 Loughrey, 2015, Digital slide viewing for primary reporting in gastrointestinal pathology: a validation study, Virchows Arch, 467, 137, 10.1007/s00428-015-1780-1 Thorstenson, 2014, Implementation of large-scale routine diagnostics using whole slide imaging in Sweden: Digital pathology experiences 2006-2013, J Pathol Inform, 5, 14, 10.4103/2153-3539.129452 Lloyd M, Kellough D, Shanks T, et al. How to acquire over 500 000 whole slides images a year: creating a massive novel data modality to accelerate cancer research. United States and Canadian Academy of Pathology Annual Meeting (USCAP); Vancouver, BC, Canada; March 20, 2018. Abstract 1647. Shrestha, 2016, A quantitative approach to evaluate image quality of whole slide imaging scanners, J Pathol Inform, 7, 56, 10.4103/2153-3539.197205 Senaras, 2018, DeepFocus: detection of out-of-focus regions in whole slide digital images using deep learning, PloS One, 13, e0205387, 10.1371/journal.pone.0205387 Lopez, 2013, An automated blur detection method for histological whole slide imaging, PloS One, 8, e82710, 10.1371/journal.pone.0082710 Shaban Komura, 2018, Machine learning methods for histopathological image analysis, Comput Struct Biotechnol J, 16, 34, 10.1016/j.csbj.2018.01.001 Zanjani 2014, Generative adversarial nets, 2672 Bentaieb, 2018, Adversarial stain transfer for histopathology image analysis, IEEE Trans Med Imaging, 37, 792, 10.1109/TMI.2017.2781228 2016 Kothari, 2014, Removing batch effects from histopathological images for enhanced cancer diagnosis, IEEE Trans Med Imaging, 18, 765 Zhu Kumar, 2017, A dataset and a technique for generalized nuclear segmentation for computational pathology, IEEE Trans Med Imaging, 36, 1550, 10.1109/TMI.2017.2677499 Xing, 2016, Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review, IEEE Rev Biomed Eng, 9, 234, 10.1109/RBME.2016.2515127 Song, 2015, Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning, IEEE Rev Biomed Eng, 62, 2421, 10.1109/TBME.2015.2430895 Xing, 2016, An automatic learning-based framework for robust nucleus segmentation, IEEE Trans Med Imaging, 35, 550, 10.1109/TMI.2015.2481436 Tremeau, 1997, A region growing and merging algorithm to color segmentation, Pattern Recognition, 30, 1191, 10.1016/S0031-3203(96)00147-1 Weinstein, 2013, The cancer genome atlas pan-cancer analysis project, Nature genetics, 45, 1113, 10.1038/ng.2764 Mahmood Yousefi, 2019, Transfer learning from nucleus detection to classification in histopathology images, BioRxiv Ren, 2015, Faster R-CNN: towards real-time object detection with region proposal networks, 91 Li, 2018, Deep mitosis: mitosis detection via deep detection, verification and segmentation networks, Medical image analysis, 45, 121, 10.1016/j.media.2017.12.002 Niazi, 2018, Automated T1 bladder risk stratification based on depth of lamina propria invasion from H&E tissue biopsies: a deep learning approach, SPIE Medical Imaging, 10581, 105810H1 Niazi, 2018, MP58–06 automated staging of T1 bladder cancer using digital pathologic H&E images: a deep learning approach, J Urol, 199, e775, 10.1016/j.juro.2018.02.1838 Niazi, 2018, Pathological image compression for big data image analysis: application to hotspot detection in breast cancer, Artif Intell Med Niazi, 2018, Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning, PloS One, 13, e0195621, 10.1371/journal.pone.0195621 Kornaropoulos, 2014, Histopathological image analysis for centroblasts classification through dimensionality reduction approaches, Cytometry A, 85, 242, 10.1002/cyto.a.22432 Sertel, 2010, Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation, IEEE Trans Biomed Eng, 57, 2613, 10.1109/TBME.2010.2055058 2016 Coudray, 2018, Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning, Nature Medicine, 24, 1559, 10.1038/s41591-018-0177-5 Tavolara, 2019, Colorectal tumor identification by tranferring knowledge from pan-cytokeratin to H&E, SPIE Medical Imaging, 10956, 1 Mairal Niazi, 2016, Hotspot detection in pancreatic neuroendocrine tumors: density approximation by α-shape maps, SPIE Medical Imaging, 9791, 97910B1 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Albarqouni, 2016, Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images, IEEE Trans Med Imaging, 35, 1313, 10.1109/TMI.2016.2528120 Niazi, 2018, Automated T1 bladder risk stratification based on depth of lamina propria invasion from H&E tissue biopsies: a deep learning approach, SPIE Medical Imaging, 10581, 105810H1 Litjens, 2016, Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Sci Rep, 6, 26286, 10.1038/srep26286 Bejnordi, 2017, Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images, J Med Imaging, 4, 044504, 10.1117/1.JMI.4.4.044504 Bejnordi, 2016, Automated detection of DCIS in whole-slide H&E stained breast histopathology images, IEEE Trans Med Imaging, 35, 2141, 10.1109/TMI.2016.2550620 Schaeffer, 2007, Graph clustering, Comp Sci Rev, 1, 27, 10.1016/j.cosrev.2007.05.001 Cruz-Roa, 2018, High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection, PloS One, 13, e0196828, 10.1371/journal.pone.0196828 Caflisch, 1998, Monte carlo and quasi-monte carlo methods, Acta Numerica, 7, 1, 10.1017/S0962492900002804 Natrajan, 2016, Microenvironmental heterogeneity parallels breast cancer progression: a histology–genomic integration analysis, PLoS Med, 13, e1001961, 10.1371/journal.pmed.1001961 Heindl, 2015, Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology, Lab Invest, 95, 377, 10.1038/labinvest.2014.155 Louis, 2014, Computational pathology: an emerging definition, Arch Pathol Lab Med, 138, 1133, 10.5858/arpa.2014-0034-ED Zaha, 2014, Significance of immunohistochemistry in breast cancer, World J Clin Oncol, 5, 382, 10.5306/wjco.v5.i3.382 Niazi, 2014, Hot spot detection for breast cancer in Ki-67 stained slides: image dependent filtering approach, SPIE Medical Imaging, 9041, 9041061 Das, 2013, Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer, PloS One, 8, e61398, 10.1371/journal.pone.0061398 Sertel O, Dogdas B, Chiu CS, Gurcan MN. Muscle histology image analysis for sarcopenia: registration of successive sections with distinct atpase activity. Biomedical imaging: from nano to macro, 2010 IEEE International Symposium; Rotterdam, Netherlands; April 14–17, 2010. Johnson, 2018, Registration parameter optimization for 3D tissue modeling from resected tumors cut into serial H&E slides, SPIE Medical Imaging, 10581, 105810T Yigitsoy, 2017, Hierarchical patch-based co-registration of differently stained histopathology slides, SPIE Medical Imaging, 10140, 1014009 Chappelow, 2011, Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information, Medical Physics, 38, 2005, 10.1118/1.3560879 Niazi, 2017, Advancing clinicopathologic diagnosis of high-risk neuroblastoma using computerized image analysis and proteomic profiling, Pediatr Dev Pathol, 20, 394, 10.1177/1093526617698603 Price Dignum, 2018, Ethics in artificial intelligence: introduction to the special issue, Ethics Inf Technol, 20, 1, 10.1007/s10676-018-9450-z