BiomedVLP-BioViL-T

模型介绍

BioViL-T is a domain-specific vision-language model designed to analyze chest X-rays (CXRs) and radiology reports. It was trained using a temporal multi-modal pre-training procedure, which distinguishes it from its predecessor model (BioViL). In detail, BioViL-T takes advantage of the temporal structure between data points, resulting in improved downstream performance on multiple benchmarks, while using the same training dataset as its predecessor. In particular, the resultant model displays significant improvement in embedding temporal information present in the image and text modalities (see results), as well as in the joint space. The canonical model can be adapted to both single- and multi-image downstream applications including: natural language inference, phrase-grounding, image/text classification, and language decoding. The corresponding BERT language model is trained in two stages: First, we pretrain CXR-BERT-general from a randomly initialized BERT model via Masked Language Modeling (MLM) on PubMed abstracts and clinical notes from the publicly-available MIMIC-III and MIMIC-CXR. The general model can be fine-tuned for research in other clinical domains by adjusting the parameters specific to the target domain. In the second stage, BioViL-T is continually pretrained from CXR-BERT-general using a multi-modal pre-training procedure by utilising radiology reports and sequences of chest X-rays. We utilise the latent representation of [CLS] token to align text and image embeddings. The image model is jointly trained with the text model in a multi-modal contrastive learning framework. It's a hybrid image encoder composed of a Vision Transformer and ResNet-50, where the latter is used as backbone network to extract features from images at each time point. The transformer is included in the design to aggregate and compare image features extracted across the temporal dimension. The corresponding model definition and its loading functions can be accessed through our HI-ML-Multimodal GitHub repository. The joint image and text model, namely BioViL-T, can be used in phrase grounding applications as shown in this python notebook example. Additionally, please check t

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。