Cui, Yufei;
Liu, Ziquan;
Chen, Yixin;
Lu, Yuchen;
Liu, Xue;
Kuo, Tei-Wei;
Rodrigues, Miguel;
... Chan, Antoni B; + view all
(2023)
Retrieval-Augmented Multiple Instance Learning.
In:
Thirty-seventh Conference on Neural Information Processing Systems - Proceedings.
Neural Information Processing Systems: New Orleans, LA, USA.
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Abstract
Multiple Instance Learning (MIL) is a crucial weakly supervised learning method applied across various domains, e.g., medical diagnosis based on whole slide images (WSIs). Recent advancements in MIL algorithms have yielded exceptional performance when the training and test data originate from the same domain, such as WSIs obtained from the same hospital. However, this paper reveals a performance deterioration of MIL models when tested on an out-of-domain test set, exemplified by WSIs sourced from a novel hospital. To address this challenge, this paper introduces the Retrieval-AugMented MIL (RAM-MIL) framework, which integrates Optimal Transport (OT) as the distance metric for nearest neighbor retrieval. The development of RAM-MIL is driven by two key insights. First, a theoretical discovery indicates that reducing the input’s intrinsic dimension can minimize the approximation error in attention-based MIL. Second, previous studies highlight a link between input intrinsic dimension and the feature merging process with the retrieved data. Empirical evaluations conducted on WSI classification demonstrate that the proposed RAM-MIL framework achieves state-of-the-art performance in both in-domain scenarios, where the training and retrieval data are in the same domain, and more crucially, in out-of-domain scenarios, where the (unlabeled) retrieval data originates from a different domain. Furthermore, the use of the transportation matrix derived from OT renders the retrieval results interpretable at the instance level, in contrast to the vanilla l2 distance, and allows for visualization for human experts.
| Type: | Proceedings paper |
|---|---|
| Title: | Retrieval-Augmented Multiple Instance Learning |
| Event: | 37th Conference on Neural Information Processing Systems (NeurIPS 2023) |
| Location: | New Orleans, United States |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://openreview.net/forum?id=scaKiAtbI3 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Multiple Instance Learning, Whole Slide Imaging, Nearest Neighbor Retrieval |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10181255 |
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