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We consider machine-learning-based thyroid-malignancy prediction from cytopathology whole-slide images (WSI).
The use of artificial intelligence in diagnosing acute cellular rejection in cardiac transplant patients, in: LABORATORY INVESTIGATION, NATURE PUBLISHING GROUP 75 VARICK ST, 9TH FLR, NEW YORK, NY 10013-1917 USA. pp. 301–301
Glass, M., Davis, R., Dov, D., Glass, C., 2020b · 1917
Earlier work this paper cites.
Regression models for ordinal data
McCullagh, P., 1980 · 1980
Earlier work this paper cites.
Statistical Inference. volume 2
Casella, G., Berger, R.L., 2002 · 2002
Earlier work this paper cites.
Categorical data analysis. volume 482
Agresti, A., 2003 · 2003
Earlier work this paper cites.
Multiple instance boosting for object detection, in: Proc. Advances in Neural Information Processing Systems (NIPS), pp. 1417–1424
Zhang, C., Platt, J.C., Viola, P., 2006 · 2006
Earlier work this paper cites.
Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely h&e-stained cytological images
Daskalakis, A., Kostopoulos, S., Spyridonos, P., Glotsos, D., Ravazoula, P., Kardari, M., Kalatzis, I., Cavouras, D., Nikiforidis, G., 2008 · 2008
Earlier work this paper cites.
The bethesda system for reporting thyroid cytopathology
Cibas, E.S., Ali, S.Z., 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al., 2009 · 2009
Earlier work this paper cites.
Cascaded learning vector quantizer neural networks for the discrimination of thyroid lesions
Varlatzidou, A., Pouliakis, A., Stamataki, M., Meristoudis, C., Margari, N., Peros, G., Panayiotides, J.G., Karakitsos, P., 2011 · 2011
Earlier work this paper cites.
Ordinal classification using hybrid artificial neural networks with projection and kernel basis functions, in: Proc. International Conference on Hybrid Artificial Intelligence Systems, Springer. pp. 319–330
Dorado-Moreno, M., Gutiérrez, P.A., Hervás-Martínez, C., 2012 · 2012
Earlier work this paper cites.
Group consensus review minimizes the diagnosis of “follicular lesion of undetermined significance” and improves cytohistologic concordance
Jing, X., Knoepp, S.M., Roh, M.H., Hookim, K., Placido, J., Davenport, R., Rasche, R., Michael, C.W., 2012 · 2012
Earlier work this paper cites.
The clinical and economic burden of a sustained increase in thyroid cancer incidence
Aschebrook-Kilfoy, B., Schechter, R.B., Shih, Y.C.T., Kaplan, E.L., Chiu, B.C.H., Angelos, P., Grogan, R.H., 2013 · 2013
Earlier work this paper cites.
Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained fnab cytological images
Gopinath, B., Shanthi, N., 2013 · 2013
Earlier work this paper cites.
Selective search for object recognition
Uijlings, J.R., Van De Sande, K.E., Gevers, T., Smeulders, A.W., 2013 · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587
Girshick, R., Donahue, J., Darrell, T., Malik, J., 2014 · 2014
Cited alongside, same era.
Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning
Ozolek, J.A., Tosun, A.B., Wang, W., Chen, C., Kolouri, S., Basu, S., Huang, H., Rohde, G.K., 2014 · 2014
Cited alongside, same era.
Implementation of the bethesda system for reporting thyroid cytopathology: interobserver concordance and reclassification of previously inconclusive aspirates
Pathak, P., Srivastava, R., Singh, N., Arora, V.K., Bhatia, A., 2014 · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A., 2014 · 2014
Cited alongside, same era.
Single-vs. multiple-instance classification
Multiple-instance learning for medical image and video analysis
Quellec, G., Cazuguel, G., Cochener, B., Lamard, M., 2017 · 2017
Later among the works it cites.
Faster r-cnn: towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J., 2017 · 2017
Later among the works it cites.
Deep learning is robust to massive label noise
Rolnick, D., Veit, A., Belongie, S., Shavit, N., 2017 · 2017
Later among the works it cites.
Deep sets, in: Proc. Advances in Neural Information Processing Systems (NIPS), pp. 3391–3401
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J., 2017 · 2017
Later among the works it cites.
Attention-based deep multiple instance learning
Ilse, M., Tomczak, J.M., Welling, M., 2018 · 2018
Later among the works it cites.
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Alpaydın, E., Cheplygina, V., Loog, M., Tax, D.M., 2015 · 2015
Cited alongside, same era.
Fast r-cnn, in: Proc. of the IEEE International Conference on Computer Vision, pp. 1440–1448
Girshick, R., 2015 · 2015
Cited alongside, same era.
Ordinal regression methods: survey and experimental study
Gutierrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., Fernandez-Navarro, F., Hervas-Martinez, C., 2016 · 2016
Cited alongside, same era.
Patch-based convolutional neural network for whole slide tissue image classification, in: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424–2433
Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H., 2016 · 2016
Cited alongside, same era.
A deep semantic mobile application for thyroid cytopathology, in: Proc. Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, International Society for Optics and Photonics. p. 97890A
Kim, E., Corte-Real, M., Baloch, Z., 2016 · 2016
Cited alongside, same era.
Classifying and segmenting microscopy images with deep multiple instance learning
Kraus, O.Z., Ba, J.L., Frey, B.J., 2016 · 2016
Cited alongside, same era.
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C.I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen-Van De Kaa, C., Bult, P., Van Ginneken, B., Van Der Laak, J., 2016 · 2016
Cited alongside, same era.
Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images
Sirinukunwattana, K., Raza, S.E.A., Tsang, Y., Snead, D.R., Cree, I.A., Rajpoot, N.M., 2016 · 2016
Cited alongside, same era.
Artificial intelligence in cytopathology: A neural network to identify papillary carcinoma on thyroid fine-needle aspiration cytology smears
Sanyal, P., Mukherjee, T., Barui, S., Das, A., Gangopadhyay, P., 2018 · 2018
Later among the works it cites.
Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid
Savala, R., Dey, P., Gupta, N., 2018 · 2018
Later among the works it cites.
A brief introduction to weakly supervised learning
Zhou, Z., 2018 · 2018
Later among the works it cites.
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M.G., Geneslaw, L., Miraflor, A., Silva, V.W.K., Busam, K.J., Brogi, E., Reuter, V.E., Klimstra, D.S., Fuchs, T.J., 2019 · 2019
Closest in time.
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Cheplygina, V., de Bruijne, M., Pluim, J.P., 2019 · 2019
Closest in time.
Thyroid cancer malignancy prediction from whole slide cytopathology images, in: Machine Learning for Healthcare Conference, pp. 553–570
Dov, D., Kovalsky, S.Z., Cohen, J., Range, D.E., Henao, R., Carin, L., 2019 · 2019
Closest in time.
Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks
Liu, T., Guo, Q., Lian, C., Ren, X., Liang, S., Yu, J., Niu, L., Sun, W., Shen, D., 2019 · 2019
Closest in time.
Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
Zhang, Z., Chen, P., McGough, M., Xing, F., Wang, C., Bui, M., Xie, Y., Sapkota, M., Cui, L., Dhillon, J., et al., 2019 · 2019
Closest in time.
Application of a machine learning algorithm to predict malignancy in thyroid cytopathology
Elliott Range, D.D., Dov, D., Kovalsky, S.Z., Henao, R., Carin, L., Cohen, J., 2020 · 2020
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