Attention-based Deep Multiple Instance Learning

2018-02-13 · ICML 2018 7 · Maximilian Ilse, Jakub M. Tomczak, Max Welling

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.

📄 PDF Abstract BibTeX arXiv:1802.04712

Code (17)

AMLab-Amsterdam/AttentionDeepMIL 공식 구현 pytorch

DhruvilKarani/mil_mnist pytorch

Hazel-4/AttentionDeepMIL pytorch

KostiukIvan/Multiple-instance-learning-with-graph-neural-networks pytorch

SeongokRyu/multiple_instance_learning tf

ThomasHenckel/Attention_Deep_MIML tf

Xiyue-Wang/RetCCL pytorch

ml-jku/hopfield-layers pytorch

mv-lab/youtube8m-19 tf

nupursjsu/EmergingTechnologiesProject tf

rameshkn/AMIL

rudrajit15/MIL-for-Breast-Cancer-Histology-Images tf

shubham808/meTP tf

srinath-niz/google_youtube8m_2019 tf

swag2198/Intern-MeDAL_IITB pytorch

ucla-starai/countloss pytorch

zhangrenyuuchicago/H-E-Image-Classification pytorch

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