Revisiting multiple instance neural networks

作者：

Highlights：

• We revisit the problem of solving MIL using neural networks (MINNs), which are ignored in current MIL research community. Our experiments show that MINNs are very effective and efficient.

• We proposed a novel MI-Net which is centered on learning bag representation in the neural networks in an end-to-end way.

• Recent deep learning tricks including dropout, deep supervision and residual connections are studied in MINNs. We find deep supervision and residual connections are effective for MIL.

• In the experiments, the proposed MINNs achieve state-of-the-art or competitive performance on several MIL benchmarks. Moreover, it is extremely fast for both testing and training, for example, it takes only 0.0003 s to predict a bag and a few seconds to train on MIL datasets on a moderate CPU.

摘要

•We revisit the problem of solving MIL using neural networks (MINNs), which are ignored in current MIL research community. Our experiments show that MINNs are very effective and efficient.•We proposed a novel MI-Net which is centered on learning bag representation in the neural networks in an end-to-end way.•Recent deep learning tricks including dropout, deep supervision and residual connections are studied in MINNs. We find deep supervision and residual connections are effective for MIL.•In the experiments, the proposed MINNs achieve state-of-the-art or competitive performance on several MIL benchmarks. Moreover, it is extremely fast for both testing and training, for example, it takes only 0.0003 s to predict a bag and a few seconds to train on MIL datasets on a moderate CPU.

论文关键词：Multiple instance learning,Neural networks,Deep learning,End-to-end learning

论文评审过程：Received 12 April 2017, Revised 24 July 2017, Accepted 23 August 2017, Available online 31 August 2017, Version of Record 15 September 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.08.026