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Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology.
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The genotype-tissue expression (gtex) pilot analysis: multitissue gene regulation in humans
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The cptac data portal: a resource for cancer proteomics research
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Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas
Network, C. G. A. R · 2015
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Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., Salakhutdinov, R. et al · 2015
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Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas
Campbell, J. D. et al · 2016
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He, K., Zhang, X., Ren, S. & Sun, J · 2016
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Comprehensive molecular characterization of papillary renal-cell carcinoma
Network, C. G. A. R · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D. et al · 2016
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Bejnordi, B. E. et al · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S. & Gupta, A · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K. & Zemel, R · 2017
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Towards a neural statistician
Edwards, H. & Storkey, A · 2017
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Wsisa: Making survival prediction from whole slide histopathological images
Zhu, X., Yao, J., Zhu, F. & Huang, J · 2017
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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Coudray, N. et al · 2018
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Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P. et al · 2018
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Bandi, P. et al · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y. & Vinyals, O · 2018
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Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
Saltz, J. et al · 2018
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Attention-based deep multiple instance learning
Ilse, M., Tomczak, J. & Welling, M · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K · 2018
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Graph cnn for survival analysis on whole slide pathological images
Li, R., Yao, J., Zhu, X., Li, Y. & Huang, J · 2018
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100,000 histological images of human colorectal cancer and healthy tissue (2018)
Kather, J. N., Halama, N. & Marx, A · 2018
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Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
Kather, J. N. et al · 2019
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Deep learning-based classification of mesothelioma improves prediction of patient outcome
Courtiol, P. et al · 2019
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Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A · 2019
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G. et al · 2019
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Large scale adversarial representation learning
Donahue, J. & Simonyan, K · 2019
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Scaling and benchmarking self-supervised visual representation learning
Goyal, P., Mahajan, D., Gupta, A. & Misra, I · 2019
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Neural image compression for gigapixel histopathology image analysis
Tellez, D., Litjens, G., van der Laak, J. & Ciompi, F · 2019
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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Kather, J. N. et al · 2019
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Bach: Grand challenge on breast cancer histology images
Aresta, G. et al · 2019
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Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Wang, Y., Chao, W.-L., Weinberger, K. Q. & Van Der Maaten, L · 2019
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Revisiting self-supervised visual representation learning
Kolesnikov, A., Zhai, X. & Beyer, L · 2019
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S4l: Self-supervised semi-supervised learning
Zhai, X., Oliver, A., Kolesnikov, A. & Beyer, L · 2019
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Spreading vectors for similarity search
Sablayrolles, A., Douze, M., Schmid, C. & Jégou, H · 2019
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Fixing the train-test resolution discrepancy
Touvron, H., Vedaldi, A., Douze, M. & Jegou, H · 2019
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Decoupled weight decay regularization
Loshchilov, I. & Hutter, F · 2019
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Infinite mixture prototypes for few-shot learning
Allen, K., Shelhamer, E., Shin, H. & Tenenbaum, J · 2019
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Deep multi-instance learning for survival prediction from whole slide images
Yao, J., Zhu, X. & Huang, J · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A. et al · 2019
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Detectron2
Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y. & Girshick, R · 2019
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Integrating spatial gene expression and breast tumour morphology via deep learning
He, B. et al · 2020
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Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
Skrede, O.-J. et al · 2020
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High-accuracy prostate cancer pathology using deep learning
Tolkach, Y., Dohmgörgen, T., Toma, M. & Kristiansen, G · 2020
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Automated deep-learning system for gleason grading of prostate cancer using biopsies: a diagnostic study
Bulten, W. et al · 2020
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Pan-cancer image-based detection of clinically actionable genetic alterations
Kather, J. N. et al · 2020
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
Fu, Y. et al · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B. et al · 2020
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Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution
Zhao, Y. et al · 2020
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Self-supervision closes the gap between weak and strong supervision in histology
Dehaene, O., Camara, A., Moindrot, O., de Lavergne, A. & Courtiol, P · 2020
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Yottixel–an image search engine for large archives of histopathology whole slide images
Kalra, S. et al · 2020
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A deep learning model to predict rna-seq expression of tumours from whole slide images
Schmauch, B. et al · 2020
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Deep learning-based survival prediction for multiple cancer types using histopathology images
Wulczyn, E. et al · 2020
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Data efficient and weakly supervised computational pathology on whole slide images
Lu, M. Y. et al · 2020
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Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma
Gillette, M. A. et al · 2020
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Language models are few-shot learners
Brown, T. et al · 2020
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Geospatial immune variability illuminates differential evolution of lung adenocarcinoma
Abdul Jabbar, K. et al · 2020
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Big transfer (bit): General visual representation learning
Kolesnikov, A. et al · 2020
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Integrative analysis of histological textures and lymphocyte infiltration in renal cell carcinoma using deep learning
Brummer, O., Polonen, P., Mustjoki, S. & Bruck, O · 2022
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Comprehensive ai model development for gleason grading: From scanning, cloud-based annotation to pathologist-ai interaction (2022)
Huo, X. et al · 2022
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Fast and scalable search of whole-slide images via self-supervised deep learning
Chen, C. et al · 2022
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Masked-attention mask transformer for universal image segmentation
Cheng, B., Misra, I., Schwing, A. G., Kirillov, A. & Girdhar, R · 2022
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Exploring plain vision transformer backbones for object detection
Li, Y., Mao, H., Girshick, R. & He, K · 2022
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Seed: Self-supervised distillation for visual representation
Fang, Z. et al · 2020
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Rethinking few-shot image classification: a good embedding is all you need?
Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B. & Isola, P · 2020
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Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N. & Huang, J · 2020
Cited alongside, same era.
Histological image tiles for tcga-crc-dx, color-normalized, sorted by msi status, train/test split (2020)
Kather, J. N · 2020
Cited alongside, same era.
Interpretable survival prediction for colorectal cancer using deep learning
Wulczyn, E. et al · 2021
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Deep learning in cancer pathology: a new generation of clinical biomarkers
Echle, A. et al · 2021
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Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (tils) predict clinical benefit for immune checkpoint inhibitors
Wang, X. et al · 2022
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Hierarchical graph representations in digital pathology
Pati, P. et al · 2022
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Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Chen, R. J. et al · 2022
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FlashAttention: Fast and memory-efficient exact attention with IO-awareness
Dao, T., Fu, D. Y., Ermon, S., Rudra, A. & Ré, C · 2022
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Slip: Self-supervision meets language-image pre-training
Mu, N., Kirillov, A., Wagner, D. & Xie, S · 2022
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Understanding collapse in non-contrastive siamese representation learning
Li, A. C., Efros, A. A. & Pathak, D · 2022
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Unsupervised visual representation learning via mutual information regularized assignment
Lee, D. H., Choi, S., Kim, H. J. & Chung, S.-Y · 2022
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A transductive learning method to leverage graph structure for few-shot learning
Wang, Y., Liu, Z., Luo, Y. & Luo, C · 2022
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Alleviating the sample selection bias in few-shot learning by removing projection to the centroid
Xu, J. et al · 2022
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The digital brain tumour atlas, an open histopathology resource (2022)
Roetzer-Pejrimovsky, T. et al · 2022
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Training data for the ”Integrative Analysis of Histological Textures and Lymphocyte Infiltration in Renal Cell Carcinoma using Deep Learning” (2022)
Brück, O. & Brummer, O · 2022
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Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer
Abousamra, S. et al · 2022
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Dataset for tumor infiltrating lymphocyte classification (304,097 image patches from TCGA) (2022)
Kaczmarzyk, J. R., Abousamra, S., Kurc, T., Gupta, R. & Saltz, J · 2022
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Machine learning in computational histopathology: Challenges and opportunities
Cooper, M., Ji, Z. & Krishnan, R. G · 2023
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