Microsoft researchers propose BioViL-T: a new self-monitoring framework that ushers in improved predictive performance and data efficiency in biomedical applications

Artificial intelligence (AI) has emerged as a significant disruptive force in numerous industries, from how tech companies operate to how innovation is unlocked in different sub-domains in the healthcare sector. In particular, the biomedical field has witnessed significant advances and transformations with the introduction of AI. One of those notable advances can be reduced to the use of self-monitored vision and language models in radiology. Radiologists rely heavily on radiology reports to convey imaging observations and provide clinical diagnoses. It is noteworthy that prior imaging studies often play a key role in this decision-making process because they provide crucial context for evaluating the course of diseases and establishing appropriate medication options. However, current on-brand AI solutions cannot successfully align images with report data due to limited access to previous scans. Furthermore, these methods often do not take into account the chronological course of disease or imaging findings that are often present in biological data sets. This lack of contextual information poses risks in downstream applications, such as automatic report generation, where models can generate inaccurate temporal content without access to previous medical examinations.

With the introduction of vision-language models, the researchers aim to generate informative training signals using image-text pairs, thus eliminating the need for manual labels. This approach allows models to learn how to accurately identify and pinpoint imaging findings and make connections to information presented in radiology reports. Microsoft Research has continually worked to improve the AI ​​for X-rays and reports. His previous research on multimodal self-supervised learning from radiology reports and images has produced encouraging results in identifying medical problems and localizing these findings within images. As a contribution to this wave of research, Microsoft released BioViL-T, a self-monitored training framework that considers previous images and reports when available during training and tuning. BioViL-T achieves breakthrough results in various downstream benchmarks, such as progression classification and reporting, by utilizing the existing temporal structure present in the data sets. The study will be presented at the prestigious Computer Vision and Pattern Recognition (CVPR) Conference in 2023.

The distinctive feature of BioViL-T lies in its explicit consideration of previous images and reports throughout the training and fitting processes, rather than treating each pair of images and reports as a separate entity. The researchers’ rationale behind incorporating previous images and reports was primarily to maximize utilization of available data, resulting in more complete representations and improved performance across a broader range of tasks. BioViL-T features a unique CNN-Transformer multi-image encoder that is co-trained with a text model. This novel multi-image encoder serves as a fundamental component of the pretraining framework, addressing challenges such as missing prior images and image variations over time.

A CNN model and a transformer were chosen to create the hybrid multi-image encoder to extract spatiotemporal features from image sequences. When previous images are available, the transformer takes care of capturing patch keying interactions over time. On the other hand, CNN is for the purpose of giving visual token properties of individual images. This hybrid image encoder improves data efficiency, making it suitable for data sets of even smaller sizes. Efficiently captures static and temporal image features, which is essential for applications such as report decoding that require dense-level visual reasoning over time. The BioViL-T model pretraining procedure can be divided into two main components: a multi-image encoder to extract spatiotemporal features, and a text encoder incorporating optional cross-attention with image features. These models are jointly trained using cross-modal global and local contrastive targets. The model also uses multimodal fused representations obtained through cross-attention for image-guided masked language modeling, thus effectively leveraging visual and textual information. This plays a central role in resolving ambiguities and improving language comprehension, which is of paramount importance for a wide range of downstream tasks.

The success of the Microsoft researchers’ strategy was aided by a variety of experimental evaluations they conducted. The model achieves state-of-the-art performance for a variety of downstream tasks such as progression categorization, phrase grounding, and reporting in single-image and multi-image configurations. Furthermore, it improves on previous models and produces measurable results on tasks such as disease classification and sentence similarity. Microsoft Research has made the model and source code publicly available to encourage the community to further investigate their work. The researchers are also making public a new multimodal temporal reference dataset called MS-CXR-T to stimulate further research to quantify how well vision-language representations can capture temporal semantics.

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