The standard non-invasive imaging technique used to assess the severity and
extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography
Angiography (CCTA). However, manual grading of each patient's CCTA according to
the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and
operator-dependent, especially in borderline cases. This work proposes a fully
automated, and visually explainable, deep learning pipeline to be used as a
decision support system for the CAD screening procedure. The pipeline performs
two classification tasks: firstly, identifying patients who require further
clinical investigations and secondly, classifying patients into subgroups based
on the degree of stenosis, according to commonly used CAD-RADS thresholds. The
pipeline pre-processes multiplanar projections of the coronary arteries,
extracted from the original CCTAs, and classifies them using a fine-tuned
Multi-Axis Vision Transformer architecture. With the aim of emulating the
current clinical practice, the model is trained to assign a per-patient score
by stacking the bi-dimensional longitudinal cross-sections of the three main
coronary arteries along channel dimension. Furthermore, it generates visually
interpretable maps to assess the reliability of the predictions. When run on a
database of 1873 three-channel images of 253 patients collected at the Monzino
Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for
the two classification tasks, respectively. According to our knowledge, this is
the first model trained to assign CAD-RADS scores learning solely from patient
scores and not requiring finer imaging annotation steps that are not part of
the clinical routine.

本研究提出了一种快速、准确的冠状动脉疾病筛查和评估方法，基于深度学习和多轴视觉变压器架构，自动对计算机断层扫描成像进行分类和评分，并不需要人工干预。

多轴视觉变换器的冠状动脉 CT 血管造影 CAD-RADS 评分：一个临床启发的深度学习流程

CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision  Transformer: a clinically-inspired deep learning pipeline

With coronary artery disease (CAD) persisting to be one of the leading causes
of death worldwide, interest in supporting physicians with algorithms to speed
up and improve diagnosis is high. In clinical practice, the severeness of CAD
is often assessed with a coronary CT angiography (CCTA) scan and manually
graded with the CAD-Reporting and Data System (CAD-RADS) score. The clinical
questions this score assesses are whether patients have CAD or not (rule-out)
and whether they have severe CAD or not (hold-out). In this work, we reach new
state-of-the-art performance for automatic CAD-RADS scoring. We propose using
severity-based label encoding, test time augmentation (TTA) and model
ensembling for a task-specific deep learning architecture. Furthermore, we
introduce a novel task- and model-specific, heuristic coronary segment
labeling, which subdivides coronary trees into consistent parts across
patients. It is fast, robust, and easy to implement. We were able to raise the
previously reported area under the receiver operating characteristic curve
(AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the
hold-out task respectively.

这份研究论文用深度学习算法实现了一种快速，可靠，易于实施的冠状动脉 CT 血管造影图标注方法，将冠状动脉树分成患者间一致的部分，并获得了比以前更好的诊断表现