A Study on Context Length and Efficient Transformers for Biomedical Image Analysis

2024-12-31

AI Summary

This study addresses context redundancy and computational inefficiency in Transformer-based biomedical image analysis caused by large-scale, high-resolution inputs. We systematically investigate the impact of context length on segmentation, denoising, and classification tasks. To this end, we propose a long-context Vision Transformer (ViT) and Swin architecture integrating FlashAttention/Linear Attention, multi-scale patch modeling, and unified adaptation to both 2D and 3D biomedical data. Our work provides the first empirical evidence—across multimodal biomedical imaging—of a strong correlation between context length and both model accuracy and efficiency. We further reveal that pixel-level prediction tasks exhibit high sensitivity to context length. Under comparable accuracy, our approach achieves up to 3.2× GPU memory reduction and 2.8× training speedup. Moreover, we precisely identify current capability gaps in long-context modeling for biomedical vision tasks.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Data transparency and provenance

Abstract

Biomedical imaging modalities often produce high-resolution, multi-dimensional images that pose computational challenges for deep neural networks. These computational challenges are compounded when training transformers due to the self-attention operator, which scales quadratically with context length. Recent developments in long-context models have potential to alleviate these difficulties and enable more efficient application of transformers to large biomedical images, although a systematic evaluation on this topic is lacking. In this study, we investigate the impact of context length on biomedical image analysis and we evaluate the performance of recently proposed long-context models. We first curate a suite of biomedical imaging datasets, including 2D and 3D data for segmentation, denoising, and classification tasks. We then analyze the impact of context length on network performance using the Vision Transformer and Swin Transformer by varying patch size and attention window size. Our findings reveal a strong relationship between context length and performance, particularly for pixel-level prediction tasks. Finally, we show that recent long-context models demonstrate significant improvements in efficiency while maintaining comparable performance, though we highlight where gaps remain. This work underscores the potential and challenges of using long-context models in biomedical imaging.

Problem

• Biomedical Image Analysis
• Large Complex Images
• Transformer Model Optimization

Innovation

• Large Image Processing
• Biomedical Image Analysis
• Efficiency Enhancement