A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification

2026-04-29

AI Summary

This study addresses the challenge of efficiently and accurately classifying 3D neuroimaging data (CT/MRI) under resource-constrained conditions. The authors systematically evaluate the performance of multiple-instance learning (MIL), 3D CNNs, and 3D Vision Transformers across several large-scale neuroimaging datasets and propose a novel MIL framework based on frozen, pre-trained 2D image encoders. Their findings demonstrate that a simple mean-pooling MIL approach achieves state-of-the-art performance on four out of six medium-scale tasks and remains competitive even on datasets comprising tens of thousands of scans, while training up to 25 times faster than more complex models. These results highlight the substantial efficiency and accuracy advantages of mean-pooling MIL without learnable attention mechanisms, offering a promising direction for lightweight medical image analysis.

Abstract

Despite being resource-intensive to train, 3D convolutional neural networks (CNNs) have been the standard approach to classify CT and MRI scans. Recent work suggests that deep multiple instance learning (MIL) may be a more efficient alternative for 3D brain scans, especially when the pre-trained image encoder used to embed each 2D slice is frozen and only the pooling operation and classifier are trained. In this paper, we provide a systematic comparison of simple MIL, attention-based MIL, 3D CNNs, and 3D ViTs across three CT and four MRI datasets, including two large datasets of at least 10,000 scans. Our goal is to help resource-constrained practitioners understand which neural networks work well for 3D neuroimages and why. We further compare design choices for attention-based MIL, including different encoders, pooling operations, and architectural orderings. We find that simple mean pooling MIL, without any learnable attention, matches or outperforms recent MIL or 3D CNN alternatives on 4 of 6 moderate-sized tasks. This baseline remains competitive on two large datasets while being 25x faster to train. To explain mean pooling's success, we examine per-slice attention quality and a semi-synthetic dataset where we can derive the best possible classifier via a Bayes estimator. This analysis reveals the limits of existing MIL approaches and suggests routes for future improvements.

Problem

• Multiple Instance Learning
• 3D Neuroimage Classification
• Computational Efficiency
• Model Comparison
• Resource-Constrained Learning

Innovation

• Multiple Instance Learning
• 3D Neuroimage Classification
• Mean Pooling
• Efficient Training
• Multi-Dataset Benchmark