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Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival can improve our understanding of patient prognosis.
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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, Scott L. Pomeroy, Todd R. Golub, Eric S. Lander, and Jill P. Mesirov · 2005
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Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients
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Cellular iron metabolism in prognosis and therapy of breast cancer
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Cyclooxygenase-2 and the inflammogenesis of breast cancer
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The molecular signatures database (msigdb) hallmark gene set collection
Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir, Mahmoud Ghandi, Jill P. Mesirov, and Pablo Tamayo · 2015
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Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Attention Is All You Need
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Tensor fusion network for multimodal sentiment analysis
Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency · 2017
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Wsisa: Making survival prediction from whole slide histopathological images
Xinliang Zhu, Jiawen Yao, Feiyun Zhu, and Junzhou Huang · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Peter Bandi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, et al · 2018
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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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Pasnet: Pathway-associated sparse deepneural network for prognosis prediction from high-throughput data
Jie Hao, Youngsoon Kim, Tae-Kyung Kim, and Mingon Kang · 2018
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub Tomczak, and Max Welling · 2018
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Using deep learning to model the hierarchical structure and function of a cell
Jianzhu Ma, Michael Ku Yu, Samson H. Fong, Keiichiro Ono, Eric Sage, Barry Demchak, Roded Sharan, and Trey Ideker · 2018
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Predicting cancer outcomes from histology and genomics using convolutional networks
Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A Gutman, Jill S Barnholtz-Sloan, José E Velázquez Vega, Daniel J Brat, and Lee AD Cooper · 2018
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Improved fusion of visual and language representations by dense symmetric co-attention for visual question answering
Kien Nguyen and Takayuki Okatani · 2018
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Gabriele Campanella, Matthew G Hanna, Luke Geneslaw, Allen Miraflor, Vitor Werneck Krauss Silva, Klaus J Busam, Edi Brogi, Victor E Reuter, David S Klimstra, and Thomas J Fuchs · 2019
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Deep learning with multimodal representation for pancancer prognosis prediction
Anika Cheerla and Olivier Gevaert · 2019
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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A. Valous, Dyke Ferber, Lina Jansen, Constantino Carlos Reyes-Aldasoro, Inka Zörnig, Dirk Jäger, Hermann Brenner, Jenny Chang-Claude, Michael Hoffmeister, and Niels Halama · 2019
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On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Multimodal transformer for unaligned multimodal language sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2019
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Multimodal multitask representation learning for pathology biobank metadata prediction
Wei-Hung Weng, Yuannan Cai, Angela Lin, Fraser Tan, and Po-Hsuan Cameron Chen · 2019
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Deep learning-based survival prediction for multiple cancer types using histopathology images
Ellery Wulczyn, David Steiner, Zhaoyang Xu, Apaar Sadhwani, Hongwu Wang, Isabelle Flament, Craig Mermel, Po-Hsuan Chen, Yun Liu, and Martin Stumpe · 2019
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan · 2020
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Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis
Richard J Chen, Ming Y Lu, Jingwen Wang, Drew FK Williamson, Scott J Rodig, Neal I Lindeman, and Faisal Mahmood · 2020
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Visualizing and interpreting cancer genomics data via the xena platform
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al · 2022
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Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge
Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen, Peter Ström, Hans Pinckaers, Kunal Nagpal, Yuannan Cai, David F Steiner, Hester van Boven, Robert Vink, et al · 2022
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Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Richard J. Chen, Ming Y. Lu, Drew F.K. Williamson, Tiffany Y. Chen, Jana Lipkova, Zahra Noor, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, and Faisal Mahmood · 2022
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FlashAttention: Fast and memory-efficient exact attention with IO-awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Generating hypergraph-based high-order representations of whole-slide histopathological images for survival prediction
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Mary J Goldman, Brian Craft, Mim Hastie, Kristupas Repečka, Fran McDade, Akhil Kamath, Ayan Banerjee, Yunhai Luo, Dave Rogers, Angela N Brooks, Jingchun Zhu, and David Haussler · 2020
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Pancancer survival analysis of cancer hallmark genes
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Blockwise self-attention for long document understanding
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Multisurv: Long-term cancer survival prediction using multimodal deep learning
Luís A. Vale-Silva and Karl Rohr · 2020
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Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks
Jiawen Yao, Xinliang Zhu, Jitendra Jonnagaddala, Nicholas Hawkins, and Junzhou Huang · 2020
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Donglin Di, Changqing Zou, Yifan Feng, Haiyan Zhou, Rongrong Ji, Qionghai Dai, and Yue Gao · 2022
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Transformer quality in linear time
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Perceiver IO: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J Henaff, Matthew Botvinick, Andrew Zisserman, Oriol Vinyals, and Joao Carreira · 2022
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Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning
Yongju Lee, Jeong Hwan Park, Sohee Oh, Kyoungseob Shin, Jiyu Sun, Minsun Jung, Cheol Lee, Hyojin Kim, Jin-Haeng Chung, Kyung Chul Moon, et al · 2022
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HFBSurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction
Ruiqing Li, Xingqi Wu, Ao Li, and Minghui Wang · 2022
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Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency · 2022
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Deep learning for survival analysis in breast cancer with whole slide image data
Huidong Liu and Tahsin Kurc · 2022
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Neural graph modelling of whole slide images for survival ranking
Callum Christopher Mackenzie, Muhammad Dawood, Simon Graham, Mark Eastwood, and Fayyaz ul Amir Afsar Minhas · 2022
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Deepsmile: Contrastive self-supervised pre-training benefits msi and hrd classification directly from h&e whole-slide images in colorectal and breast cancer
Yoni Schirris, Efstratios Gavves, Iris Nederlof, Hugo Mark Horlings, and Jonas Teuwen · 2022
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Transformers in medical imaging: A survey
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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artem Shmatko, Narmin Ghaffari Laleh, Moritz Gerstung, and Jakob Nikolas Kather · 2022
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Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images
Suzanne Wetstein, Vincent Jong, Nikolas Stathonikos, Mark Opdam, Gwen Dackus, Josien Pluim, Paul Diest, and Mitko Veta · 2022
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Flowformer: Linearizing transformers with conservation flows
Haixu Wu, Jialong Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2022
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Multimodal learning with transformers: A survey, 2022
Peng Xu, Xiatian Zhu, and David Clifton · 2022
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FlashAttention-2: Faster attention with better parallelism and work partitioning
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