ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
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Clinical imaging databases contain not only medical images but also text reports generated by physicians. These narrative reports often describe the location, size, and shape of the disease, but using descriptive text to guide medical image analysis has been understudied. Vision-language models are increasingly used for multimodal tasks like image generation, image captioning, and visual question answering but have been scarcely used in medical imaging. In this work, we develop a vision-language model for the task of pneumothorax segmentation. Our model, ConTEXTual Net, detects and segments pneumothorax in chest radiographs guided by free-form radiology reports. ConTEXTual Net achieved a Dice score of 0.72 ± 0.02, which was similar to the level of agreement between the primary physician annotator and the other physician annotators (0.71 ± 0.04). ConTEXTual Net also outperformed a U-Net. We demonstrate that descriptive language can be incorporated into a segmentation model for improved performance. Through an ablative study, we show that it is the text information that is responsible for the performance gains. Additionally, we show that certain augmentation methods worsen ConTEXTual Net's segmentation performance by breaking the image-text concordance. We propose a set of augmentations that maintain this concordance and improve segmentation training.
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