End-to-end Multiple Instance Learning with Gradient Accumulation

• URL: http://arxiv.org/abs/2203.03981v1
• Date: Tue, 8 Mar 2022 10:14:51 GMT
• Title: End-to-end Multiple Instance Learning with Gradient Accumulation
• Authors: Axel Andersson, Nadezhda Koriakina, Nata\v{s}a Sladoje and Joakim Lindblad
• Abstract summary: We propose a training strategy that enables end-to-end training of ABMIL models without being limited by GPU memory. We conduct experiments on both QMNIST and Imagenette to investigate the performance and training time. This memory-efficient approach, although slower, reaches performance indistinguishable from the memory-expensive baseline.
• Score: 2.2612425542955292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Being able to learn on weakly labeled data, and provide interpretability, are two of the main reasons why attention-based deep multiple instance learning (ABMIL) methods have become particularly popular for classification of histopathological images. Such image data usually come in the form of gigapixel-sized whole-slide-images (WSI) that are cropped into smaller patches (instances). However, the sheer size of the data makes training of ABMIL models challenging. All the instances from one WSI cannot be processed at once by conventional GPUs. Existing solutions compromise training by relying on pre-trained models, strategic sampling or selection of instances, or self-supervised learning. We propose a training strategy based on gradient accumulation that enables direct end-to-end training of ABMIL models without being limited by GPU memory. We conduct experiments on both QMNIST and Imagenette to investigate the performance and training time, and compare with the conventional memory-expensive baseline and a recent sampled-based approach. This memory-efficient approach, although slower, reaches performance indistinguishable from the memory-expensive baseline.

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