arXiv:2605.27306v1 Announce Type: new Abstract: We consider training classifiers for 3D medical images using only one binary label for the entire volume rather than a label for each 2D slice. In such weakly supervised settings, can we learn accurate classifiers for slice-level predictions? Attention-based multiple instance learning (MIL) can produce an attention score for every slice. Yet recent work demonstrates that a simple center-focused baseline that ignores image content can outperform attention-based and transformer-based MIL at slice-level classification of 3D brain scans. We show this
This research is emerging as the AI community continues to refine and optimize attention mechanisms for complex data types like 3D medical images.
It challenges the conventional wisdom that complex attention models always outperform simpler baselines, suggesting avenues for more efficient and robust AI in critical applications.
The understanding of attention in AI systems for medical imaging is refined, emphasizing the importance of 'normal guidance' over convoluted attention mechanisms in certain contexts.
- · Medical AI developers
- · Healthcare providers
- · Patients (through improved diagnostics)
- · Efficient AI model design
- · Overspecified attention-based MIL models
- · Developers solely focused on increasing model complexity
It will prompt a re-evaluation of attention mechanisms in medical image analysis and possibly other domains.
This could lead to a new generation of AI diagnostic tools that are simpler, more robust, and easier to deploy.
The principle of 'normal guidance' might be generalized to other complex deep learning tasks, simplifying model architectures across various AI applications.
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Read at arXiv cs.LG