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Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances.
Integrated segmentation and recognition of hand-printed numerals
Keeler, James D, Rumelhart, David E, and Leow, Wee Kheng · 1991
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Solving the multiple instance problem with axis-parallel rectangles
Dietterich, Thomas G, Lathrop, Richard H, and Lozano-Pérez, Tomás · 1997
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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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A framework for multiple-instance learning
Maron, Oded and Lozano-Pérez, Tomás · 1998
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Multi instance neural networks
Ramon, Jan and De Raedt, Luc · 2000
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Quantification of histochemical staining by color deconvolution
Ruifrok, Arnout C and Johnston, Dennis A · 2001
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Multi-instance kernels
Gärtner, Thomas, Flach, Peter A, Kowalczyk, Adam, and Smola, Alexander J · 2002
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Em-dd: An improved multiple-instance learning technique
Zhang, Qi and Goldman, Sally A · 2002
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Support vector machines for multiple-instance learning
Andrews, Stuart, Tsochantaridis, Ioannis, and Hofmann, Thomas · 2003
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On generalized multiple-instance learning
Scott, Stephen, Zhang, Jun, and Brown, Joshua · 2005
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MILES: Multiple-instance learning via embedded instance selection
Chen, Yixin, Bi, Jinbo, and Wang, James Ze · 2006
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Multiple instance boosting for object detection
Zhang, Cha, Platt, John C, and Viola, Paul A · 2006
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Identification and expansion of human colon-cancer-initiating cells
Ricci-Vitiani, Lucia, Lombardi, Dario G, Pilozzi, Emanuela, Biffoni, Mauro, Todaro, Matilde, Peschle, Cesare, and De Maria, Ruggero · 2007
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Evaluation and benchmark for biological image segmentation
Gelasca, Elisa Drelie, Byun, Jiyun, Obara, Boguslaw, and Manjunath, BS · 2008
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Bayesian multiple instance learning: automatic feature selection and inductive transfer
Raykar, Vikas C, Krishnapuram, Balaji, Bi, Jinbo, Dundar, Murat, and Rao, R Bharat · 2008
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Multi-instance learning by treating instances as non-iid samples
Zhou, Zhi-Hua, Sun, Yu-Yin, and Li, Yu-Feng · 2009
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks
Glorot, Xavier and Bengio, Yoshua · 2010
Cited alongside, same era.
Key instance detection in multi-instance learning
Liu, Guoqing, Wu, Jianxin, and Zhou, Zhi-Hua · 2012
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Bahdanau, Dzmitry, Cho, Kyunghyun, and Bengio, Yoshua · 2014
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A theoretical and empirical analysis of support vector machine methods for multiple-instance classification
Doran, Gary and Ray, Soumya · 2014
Cited alongside, same era.
Empowering multiple instance histopathology cancer diagnosis by cell graphs
Variational Weakly Supervised Gaussian Processes
Kandemir, Melih, Haußmann, Manuel, Diego, Ferran, Rajamani, Kumar T, van der Laak, Jeroen, and Hamprecht, Fred A · 2016
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Classifying and segmenting microscopy images with deep multiple instance learning
Kraus, Oren Z, Ba, Jimmy Lei, and Frey, Brendan J · 2016
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Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images
Sirinukunwattana, Korsuk, Raza, Shan E Ahmed, Tsang, Yee-Wah, Snead, David RJ, Cree, Ian A, and Rajpoot, Nasir M · 2016
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Revisiting multiple instance neural networks
Wang, Xinggang, Yan, Yongluan, Tang, Peng, Bai, Xiang, and Liu, Wenyu · 2016
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Deep MIML Network
Feng, Ji and Zhou, Zhi-Hua · 2017
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A structured self-attentive sentence embedding
Lin, Zhouhan, Feng, Minwei, Santos, Cicero Nogueira dos, Yu, Mo, Xiang, Bing, Zhou, Bowen, and Bengio, Yoshua · 2017
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Kandemir, Melih, Zhang, Chong, and Hamprecht, Fred A · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, Diederik P and Ba, Jimmy · 2014
Cited alongside, same era.
Weakly supervised object recognition with convolutional neural networks
Oquab, Maxime, Bottou, Léon, Laptev, Ivan, Sivic, Josef, et al · 2014
Cited alongside, same era.
Explaining the stars: Weighted multiple-instance learning for aspect-based sentiment analysis
Pappas, Nikolaos and Popescu-Belis, Andrei · 2014
Cited alongside, same era.
Computer-aided diagnosis from weak supervision: a benchmarking study
Kandemir, Melih and Hamprecht, Fred A · 2015
Cited alongside, same era.
From image-level to pixel-level labeling with convolutional networks
Pinheiro, Pedro O and Collobert, Ronan · 2015
Cited alongside, same era.
Feed-forward networks with attention can solve some long-term memory problems
Raffel, Colin and Ellis, Daniel PW · 2015
Cited alongside, same era.
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A survey on deep learning in medical image analysis
Litjens, Geert, Kooi, Thijs, Bejnordi, Babak Ehteshami, Setio, Arnaud Arindra Adiyoso, Ciompi, Francesco, Ghafoorian, Mohsen, van der Laak, Jeroen A.W.M., van Ginneken, Bram, and Sánchez, Clara I · 2017
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Explicit Document Modeling through Weighted Multiple-Instance Learning
Pappas, Nikolaos and Popescu-Belis, Andrei · 2017
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PointNet: Deep learning on point sets for 3d classification and segmentation
Qi, Charles R, Su, Hao, Mo, Kaichun, and Guibas, Leonidas J · 2017
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Multiple-instance learning for medical image and video analysis
Quellec, Gwenole, Cazuguel, Guy, Cochener, Beatrice, and Lamard, Mathieu · 2017
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Residual Attention Network for Image Classification
Wang, Fei, Jiang, Mengqing, Qian, Chen, Yang, Shuo, Li, Cheng, Zhang, Honggang, Wang, Xiaogang, and Tang, Xiaoou · 2017
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Scalable algorithms for multi-instance learning
Wei, Xiu-Shen, Wu, Jianxin, and Zhou, Zhi-Hua · 2017
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Deep Sets
Zaheer, Manzil, Kottur, Satwik, Ravanbakhsh, Siamak, Poczos, Barnabas, Salakhutdinov, Ruslan, and Smola, Alexander · 2017
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Deep multi-instance networks with sparse label assignment for whole mammogram classification
Zhu, Wentao, Lou, Qi, Vang, Yeeleng Scott, and Xie, Xiaohui · 2017
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