Pathology detection and delineation enables the automatic interpretation of
medical scans such as chest X-rays while providing a high level of
explainability to support radiologists in making informed decisions. However,
annotating pathology bounding boxes is a time-consuming task such that large
public datasets for this purpose are scarce. Current approaches thus use weakly
supervised object detection to learn the (rough) localization of pathologies
from image-level annotations, which is however limited in performance due to
the lack of bounding box supervision. We therefore propose anatomy-driven
pathology detection (ADPD), which uses easy-to-annotate bounding boxes of
anatomical regions as proxies for pathologies. We study two training
approaches: supervised training using anatomy-level pathology labels and
multiple instance learning (MIL) with image-level pathology labels. Our results
show that our anatomy-level training approach outperforms weakly supervised
methods and fully supervised detection with limited training samples, and our
MIL approach is competitive with both baseline approaches, therefore
demonstrating the potential of our approach.

病理检测和描述能够自动解释医学扫描，如胸部 X 线片，同时提供高水平的可解释性，以支持放射科医生做出明智决策。

基于解剖学的胸部 X 射线病理检测

Anatomy-Driven Pathology Detection on Chest X-rays

This work introduces a simple deep-learning based method to delineate
contours by `walking' along learnt unit vector fields. We demonstrate the
effectiveness of our pipeline on the unique case of open contours on the task
of delineating the sacroiliac joints (SIJs) in spinal MRIs. We show that: (i)
95% of the time the average root mean square error of the predicted contour
against the original ground truth is below 4.5 pixels (2.5mm for a standard
T1-weighted SIJ MRI), and (ii) the proposed method is better than the baseline
of regressing vertices or landmarks of contours.

本文介绍了一种基于深度学习的简单方法，通过 “行走” 单位向量场来描绘轮廓，取得了成功效果于脊柱磁共振图像（MRI）描绘骶髂关节（SIJs）的独特情况中。我们证明：（i）95% 的时间，预测轮廓的平均均方根误差与原始基础事实在 4.5 像素以下（标准 T1 加权 SIJ MRI 为 2.5mm），（ii）所提出的方法比轮廓的顶点或基准的回归更好。

通过单位向量场回归进行轮廓分割

Contouring by Unit Vector Field Regression

Accurate delineation of key waveforms in an ECG is a critical initial step in
extracting relevant features to support the diagnosis and treatment of heart
conditions. Although deep learning based methods using a segmentation model to
locate P, QRS and T waves have shown promising results, their ability to handle
signals exhibiting arrhythmia remains unclear. In this study, we propose a
novel approach that leverages a deep learning model to accurately delineate
signals with a wide range of arrhythmia. Our approach involves training a
segmentation model using a hybrid loss function that combines segmentation with
the task of arrhythmia classification. In addition, we use a diverse training
set containing various arrhythmia types, enabling our model to handle a wide
range of challenging cases. Experimental results show that our model accurately
delineates signals with a broad range of abnormal rhythm types, and the
combined training with classification guidance can effectively reduce false
positive P wave predictions, particularly during atrial fibrillation and atrial
flutter. Furthermore, our proposed method shows competitive performance with
previous delineation algorithms on the Lobachevsky University Database (LUDB).

本研究提出了一种基于深度学习模型来精确勾画具有不同心律失常类型信号的新方法，使用了一种混合损失函数的分割模型用于心律失常分类，实验结果表明该模型与先前的勾画算法在 Lobachevsky University Database (LUDB) 上表现竞争力。