Previous deep learning efforts have focused on improving the performance of
Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using
Convolutional Neural Networks (CNN). However, the features from CT scans alone
are not always sufficient for the diagnosis of PE. CT scans along with
electronic heath records (EHR) can provide a better insight into the patients
condition and can lead to more accurate PE diagnosis. In this paper, we propose
Pulmonary Embolism Detection using Contrastive Learning (PECon), a supervised
contrastive pretraining strategy that employs both the patients CT scans as
well as the EHR data, aiming to enhance the alignment of feature
representations between the two modalities and leverage information to improve
the PE diagnosis. In order to achieve this, we make use of the class labels and
pull the sample features of the same class together, while pushing away those
of the other class. Results show that the proposed work outperforms the
existing techniques and achieves state-of-the-art performance on the RadFusion
dataset with an F1-score of 0.913, accuracy of 0.90 and an AUROC of 0.943.
Furthermore, we also explore the explainability of our approach in comparison
to other methods. Our code is publicly available at
this https URL

使用对比学习的肺栓塞检测方法（PECon）结合患者 CT 扫描和电子健康记录，旨在提高两种模态之间特征表示的对齐性，从而改善肺栓塞的诊断效果，并通过实验结果表明该方法在 RadFusion 数据集上表现优于现有技术，得到了 0.913 的 F1 分数，0.90 的准确率和 0.943 的 AUROC。

PECon: 提升 CT 和 EHR 数据特征对齐以改进肺栓塞诊断的对比预训练

PECon: Contrastive Pretraining to Enhance Feature Alignment between CT  and EHR Data for Improved Pulmonary Embolism Diagnosis

A universal classification model aims to generalize to diverse classification
tasks in both zero and few shot settings. A promising way toward universal
classification is to cast heterogeneous data formats into a dataset-agnostic
"meta-task" (e.g., textual entailment, question answering) then pretrain a
model on the combined meta dataset. The existing work is either pretrained on
specific subsets of classification tasks, or pretrained on both classification
and generation data but the model could not fulfill its potential in
universality and reliability. These also leave a massive amount of annotated
data under-exploited. To fill these gaps, we propose ConEntail, a new framework
for universal zero and few shot classification with supervised contrastive
pretraining. Our unified meta-task for classification is based on nested
entailment. It can be interpreted as "Does sentence a entails [sentence b
entails label c]". This formulation enables us to make better use of 57
annotated classification datasets for supervised contrastive pretraining and
universal evaluation. In this way, ConEntail helps the model (1) absorb
knowledge from different datasets, and (2) gain consistent performance gain
with more pretraining data. In experiments, we compare our model with
discriminative and generative models pretrained on the same dataset. The
results confirm that our framework effectively exploits existing annotated data
and consistently outperforms baselines in both zero (9.4% average improvement)
and few shot settings (3.5% average improvement).

提出了一种基于超级对比学习预处理的新框架 ConEntail，它使用统一的元任务进行分类，该任务基于嵌套蕴含。实验结果表明，该框架可以有效地利用现有的注释数据，在零和少量样本情况下始终优于基线 (分别达到 9.4% 和 3.5% 的平均改进)。