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Grading precancerous lesions on whole slide images is a challenging task: the continuous space of morphological phenotypes makes clear-cut decisions between different grades often difficult, leading to low inter- and intra-rater agreements.
Evaluation of a new grading system for laryngeal squamous intraepithelial lesions—a proposed unified classification
Nina Gale, Rok Blagus, Samir K El-Mofty, Tim Helliwell, Manju L Prasad, Ann Sandison, Metka Volavšek, Bruce M Wenig, Nina Zidar, and Antonio Cardesa · 2014
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Cost-aware pre-training for multiclass cost-sensitive deep learning
Yu-An Chung, Hsuan-Tien Lin, and Shao-Wen Yang · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout
Ian Osband · 2016
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, and others · 2017
Earlier work this paper cites.
WHO classification of head and neck tumours
Adel K El-Naggar, John KC Chan, Jennifer R Grandis, and others · 2017
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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Nicolas Coudray, Paolo Santiago Ocampo, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, David Fenyö, Andre L Moreira, Narges Razavian, and Aristotelis Tsirigos · 2018
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub Tomczak, and Max Welling · 2018
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Laryngeal precursor lesions: Interrater and intrarater reliability of histopathological assessment
Camilla Slot Mehlum, Stine Rosenkilde Larsen, Katalin Kiss, Aagot Moeller Groentved, Thomas Kjaergaard, Sören Möller, and Christian Godballe · 2018
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Deep learning-based classification of mesothelioma improves prediction of patient outcome
Pierre Courtiol, Charles Maussion, Matahi Moarii, Elodie Pronier, Samuel Pilcer, Meriem Sefta, Pierre Manceron, Sylvain Toldo, Mikhail Zaslavskiy, Nolwenn Le Stang, and others · 2019
Cited alongside, same era.
Deep active learning for axon-myelin segmentation on histology data
Melanie Lubrano di Scandalea, Christian S Perone, Mathieu Boudreau, and Julien Cohen-Adad · 2019
Cited alongside, same era.
Quantitative comparison of monte-carlo dropout uncertainty measures for multi-class segmentation
Robin Camarasa, Daniel Bos, Jeroen Hendrikse, Paul Nederkoorn, Eline Kooi, Aad van der Lugt, and Marleen de Bruijne · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Laryngeal dysplasia: persisting dilemmas, disagreements and unsolved problems—a short review
Nina Gale, Antonio Cardesa, Juan C Hernandez-Prera, Pieter J Slootweg, Bruce M Wenig, and Nina Zidar · 2020
Cited alongside, same era.
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Yogesh K Dwivedi, Laurie Hughes, Elvira Ismagilova, Gert Aarts, Crispin Coombs, Tom Crick, Yanqing Duan, Rohita Dwivedi, John Edwards, Aled Eirug, and others · 2021
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Understanding softmax confidence and uncertainty
Tim Pearce, Alexandra Brintrup, and Jun Zhu · 2021
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Deep learning in histopathology: the path to the clinic
Jeroen Van der Laak, Geert Litjens, and Francesco Ciompi · 2021
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Assessment of deep learning assistance for the pathological diagnosis of gastric cancer
Wei Ba, Shuhao Wang, Meixia Shang, Ziyan Zhang, Huan Wu, Chunkai Yu, Ranran Xing, Wenjuan Wang, Lang Wang, Cancheng Liu, and others · 2022
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Impact of a deep learning assistant on the histopathologic classification of liver cancer
Amirhossein Kiani, Bora Uyumazturk, Pranav Rajpurkar, Alex Wang, Rebecca Gao, Erik Jones, Yifan Yu, Curtis P Langlotz, Robyn L Ball, Thomas J Montine, and others · 2020
Cited alongside, same era.
Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks
Jasper Linmans, Jeroen van der Laak, and Geert Litjens · 2020
Cited alongside, same era.
Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
Tanya Nair, Doina Precup, Douglas L Arnold, and Tal Arbel · 2020
Cited alongside, same era.
Can you trust predictive uncertainty under real dataset shifts in digital pathology?
Jeppe Thagaard, Søren Hauberg, Bert van der Vegt, Thomas Ebstrup, Johan D Hansen, and Anders B Dahl · 2020
Cited alongside, same era.
James M Dolezal, Andrew Srisuwananukorn, Dmitry Karpeyev, Siddhi Ramesh, Sara Kochanny, Brittany Cody, Aaron Mansfield, Sagar Rakshit, Radhika Bansa, Melanie Bois, and others · 2022
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Can ai predict epithelial lesion categories via automated analysis of cervical biopsies: The tissuenet challenge?
Nicolas Loménie, Capucine Bertrand, Rutger HJ Fick, Saima Ben Hadj, Brice Tayart, Cyprien Tilmant, Isabelle Farré, Soufiane Z Azdad, Samy Dahmani, Gilles Dequen, et al · 2022
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Diagnosis with Confidence: Deep Learning for Reliable Classification of Squamous Lesions of the Upper Aerodigestive Tract
Mélanie Lubrano, Yaëlle Bellahsen-Harrar, Sylvain Berlemont, Thomas Walter, and Cécile Badoual · 2022
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Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology
Milda Poceviciute, Gabriel Eilertsen, Sofia Jarkman, and Claes Lundström · 2022
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