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Simple MIL matches complex models for 3D neuroimage classification

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have published a benchmark comparing multiple instance learning (MIL) methods against 3D CNNs and ViTs for classifying 3D neuroimages. The study found that a simple mean pooling MIL approach, without attention mechanisms, performed comparably or better than more complex methods on several tasks. This baseline MIL method was also significantly faster to train, making it a viable option for practitioners with limited computational resources. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Provides a benchmark for efficient neural network selection in 3D neuroimage classification, aiding resource-constrained practitioners.

RANK_REASON Academic paper comparing different machine learning architectures on a specific task.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Ethan Harvey, Dennis Johan Loevlie, Amir Ali Satani, Wansu Chen, David M. Kent, Michael C. Hughes ·

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

    arXiv:2604.26807v1 Announce Type: new 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…

  2. arXiv cs.LG TIER_1 · Michael C. Hughes ·

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

    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…

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