Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing (2204.09817v4)

Abstract: Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional challenges in vision--LLMling compared to the general domain, and previous work has used insufficiently adapted models that lack domain-specific language understanding. In this paper, we show that principled textual semantic modelling can substantially improve contrastive learning in self-supervised vision--language processing. We release a LLM that achieves state-of-the-art results in radiology natural language inference through its improved vocabulary and novel language pretraining objective leveraging semantics and discourse characteristics in radiology reports. Further, we propose a self-supervised joint vision--language approach with a focus on better text modelling. It establishes new state of the art results on a wide range of publicly available benchmarks, in part by leveraging our new domain-specific LLM. We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision--language processing. A broad evaluation, including on this new dataset, shows that our contrastive learning approach, aided by textual-semantic modelling, outperforms prior methods in segmentation tasks, despite only using a global-alignment objective.

Improving Biomedical Vision-Language Processing with Enhanced Text Semantics

The paper "Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing" presents an innovative approach to enhancing vision-language processing (VLP) in the biomedical field, particularly radiology. The paper underscores the importance of refined textual semantic modeling in improving contrastive learning techniques, a critical component of self-supervised vision-LLMs.

Key Contributions

The paper introduces a specialized LLM known as CXR-BERT, optimized for the radiology domain. This model sets new benchmarks in radiology natural language inference by leveraging an enhanced vocabulary and novel language pretraining that incorporates the semantics and discourse patterns prevalent in radiology reports. Moreover, the authors propose a self-supervised multi-modal VLP approach, termed BioViL, which prioritizes robust text modeling to improve joint image-text representations.

1. LLM Advancements: CXR-BERT, with its domain-specific enhancements, shows significant improvements in text understanding. It incorporates innovative pretraining phases that extend beyond traditional models like ClinicalBERT and PubMedBERT. This focus on semantic depth enhances performance in tasks such as masked token prediction and natural language inference.
2. Vision-LLM Development: The researchers develop BioViL, a VLP framework that extends the efficacy of CXR-BERT into multi-modal contexts. BioViL demonstrates state-of-the-art performance across numerous publicly available benchmarks, emphasizing zero-shot and fine-tuned classifications, as well as segmentation tasks.
3. Dataset Contribution: The creation of MS-CXR, a dataset enriched with locally-aligned phrase grounding annotations by radiologists, advances the paper of complex semantic modeling. This dataset supports a more nuanced evaluation of text-image alignment and enhances model training in capturing intricate biomedical semantics.

Technical Insights

The paper highlights the technical complexities involved in biomedical text processing, such as dealing with negations, domain-specific terminology, and the indispensability of long-range dependencies in textual descriptions. The authors address these challenges with dedicated pretraining strategies that significantly enhance the LLM's ability to understand and predict domain-specific language constructs, thus contributing to more effective text-image relationship modeling.

Evaluation and Impact

Evaluations reveal that the text-modal enhancements lead to substantial performance improvements in both global and local alignment tasks. Specifically, by refining text semantics, the BioViL model surpasses existing methods in zero-shot classification and segmentation tasks without extensive manual annotations or local loss terms during training. This suggests a shift towards less annotation-dependent yet highly efficient VLP models in the medical domain.

Implications and Future Directions

This research opens avenues for developing highly specialized models tailored to niche domains like radiology, where linguistic nuances are clinically significant. Future work could explore expanding such models across other medical imaging modalities or integrating them into real-world clinical decision-support systems, enhancing the efficacy and reliability of automated diagnostics. Moreover, the concepts applied in CXR-BERT and BioViL advocate for broader application in domains where complex text semantics intersect with nuanced visual data, highlighting the potential of such integrative approaches in AI advancements.