Radiologists highly desire fully automated versatile AI for medical imaging
interpretation. However, the lack of extensively annotated large-scale
multi-disease datasets has hindered the achievement of this goal. In this
paper, we explore the feasibility of leveraging language as a naturally
high-quality supervision for chest CT imaging. In light of the limited
availability of image-report pairs, we bootstrap the understanding of 3D chest
CT images by distilling chest-related diagnostic knowledge from an extensively
pre-trained 2D X-ray expert model. Specifically, we propose a language-guided
retrieval method to match each 3D CT image with its semantically closest 2D
X-ray image, and perform pair-wise and semantic relation knowledge
distillation. Subsequently, we use contrastive learning to align images and
reports within the same patient while distinguishing them from the other
patients. However, the challenge arises when patients have similar semantic
diagnoses, such as healthy patients, potentially confusing if treated as
negatives. We introduce a robust contrastive learning that identifies and
corrects these false negatives. We train our model with over 12,000 pairs of
chest CT images and radiology reports. Extensive experiments across multiple
scenarios, including zero-shot learning, report generation, and fine-tuning
processes, demonstrate the model's feasibility in interpreting chest CT images.

利用自然语言作为有限可用的数据集辅助胸部 CT 图像理解并通过对比学习技术提高模型的性能。

通过从 X 射线专家模型中提炼知识来引导胸部 CT 图像理解

Bootstrapping Chest CT Image Understanding by Distilling Knowledge from  X-ray Expert Models

Automated segmentation of lung abnormalities in computed tomography is an
important step for diagnosing and characterizing lung disease. In this work, we
improve upon a previous method and propose S-MEDSeg, a deep learning based
approach for accurate segmentation of lung lesions in chest CT images. S-MEDSeg
combines a pre-trained EfficientNet backbone, bidirectional feature pyramid
network, and modern network advancements to achieve improved segmentation
performance. A comprehensive ablation study was performed to evaluate the
contribution of the proposed network modifications. The results demonstrate
modifications introduced in S-MEDSeg significantly improves segmentation
performance compared to the baseline approach. The proposed method is applied
to an independent dataset of long COVID inpatients to study the effect of
post-acute infection vaccination on extent of lung findings. Open-source code,
graphical user interface and pip package are available at
this https URL

本文介绍了 S-MEDSeg，一种基于深度学习的方法，通过改进已有方法实现了精确分割胸部 CT 图像中的肺部病变，该方法的网络改进显著提高了分割性能，并在研究长期 COVID 住院患者的数据集中应用，研究了后急性感染疫苗对肺部病变程度的影响。

CT 中肺部发现的自动分割及其在长期 COVID 病中的应用

Automatic segmentation of lung findings in CT and application to Long  COVID

The current pandemic, caused by the outbreak of a novel coronavirus
(COVID-19) in December 2019, has led to a global emergency that has
significantly impacted economies, healthcare systems and personal wellbeing all
around the world. Controlling the rapidly evolving disease requires highly
sensitive and specific diagnostics. While real-time RT-PCR is the most commonly
used, these can take up to 8 hours, and require significant effort from
healthcare professionals. As such, there is a critical need for a quick and
automatic diagnostic system. Diagnosis from chest CT images is a promising
direction. However, current studies are limited by the lack of sufficient
training samples, as acquiring annotated CT images is time-consuming. To this
end, we propose a new deep learning algorithm for the automated diagnosis of
COVID-19, which only requires a few samples for training. Specifically, we use
contrastive learning to train an encoder which can capture expressive feature
representations on large and publicly available lung datasets and adopt the
prototypical network for classification. We validate the efficacy of the
proposed model in comparison with other competing methods on two publicly
available and annotated COVID-19 CT datasets. Our results demonstrate the
superior performance of our model for the accurate diagnosis of COVID-19 based
on chest CT images.

提出一个新的深度学习算法来自动诊断 COVID-19，该算法只需要少量样本进行训练，使用对比学习训练编码器来捕获表达性特征表示，并采用代表性网络进行分类。在两个公开可用的已注释 COVID-19 CT 数据集上，我们的结果表明我们的模型在基于胸部 CT 图像的 COVID-19 精确诊断方面表现出优越性能。