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Attention-based Deep Multiple Instance Learning
TL;DR: In this paper, a neural network-based permutation-invariant aggregation operator is proposed to learn the Bernoulli distribution of the bag label, where the bag-label probability is fully parameterized by neural networks.
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Abstract: Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. Notably, an application of the proposed attention-based operator provides insight into the contribution of each instance to the bag label. We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.
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Citations
Data-efficient and weakly supervised computational pathology on whole-slide images.
Ming Y. Lu,Ming Y. Lu,Ming Y. Lu,Drew F. K. Williamson,Tiffany Y. Chen,Richard J. Chen,Richard J. Chen,Matteo Barbieri,Matteo Barbieri,Faisal Mahmood,Faisal Mahmood,Faisal Mahmood +11 more
TL;DR: In this article, a clustering-constrained-attention multiple-instance learning (CLAM) method is proposed to identify subregions of high diagnostic value to accurately classify whole slides and instance level clustering over the identified representative regions to constrain and refine the feature space.
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End-to-End Learning of Visual Representations From Uncurated Instructional Videos
Antoine Miech,Jean-Baptiste Alayrac,Lucas Smaira,Ivan Laptev,Josef Sivic,Andrew Zisserman +5 more
- 14 Jun 2020
TL;DR: This work proposes a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos and outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines.
Deep learning in histopathology: the path to the clinic
TL;DR: In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading, but despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques as discussed by the authors.
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Deep neural network models for computational histopathology: A survey
TL;DR: A comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis can be found in this paper, where a survey of over 130 papers is presented.
542
Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling
Jiancheng Yang,Qiang Zhang,Bingbing Ni,Linguo Li,Jinxian Liu,Mengdie Zhou,Qi Tian +6 more
- 01 Jun 2019
TL;DR: This work develops Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention, and proposes an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points.
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A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
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