The widespread emergence of smart devices for ECG has sparked demand for
intelligent single-lead ECG-based diagnostic systems. However, it is
challenging to develop a single-lead-based ECG interpretation model for
multiple diseases diagnosis due to the lack of some key disease information. In
this work, we propose inter-lead Multi-View Knowledge Transferring of ECG
(MVKT-ECG) to boost single-lead ECG's ability for multi-label disease
diagnosis. This training strategy can transfer superior disease knowledge from
multiple different views of ECG (e.g. 12-lead ECG) to single-lead-based ECG
interpretation model to mine details in single-lead ECG signals that are easily
overlooked by neural networks. MVKT-ECG allows this lead variety as a
supervision signal within a teacher-student paradigm, where the teacher
observes multi-lead ECG educates a student who observes only single-lead ECG.
Since the mutual disease information between the single-lead ECG and muli-lead
ECG plays a key role in knowledge transferring, we present a new disease-aware
Contrastive Lead-information Transferring(CLT) to improve the mutual disease
information between the single-lead ECG and muli-lead ECG. Moreover, We modify
traditional Knowledge Distillation to multi-label disease Knowledge
Distillation (MKD) to make it applicable for multi-label disease diagnosis. The
comprehensive experiments verify that MVKT-ECG has an excellent performance in
improving the diagnostic effect of single-lead ECG.

提出了一种新的单导联心电信号的多标记疾病诊断方法，即基于多视角知识转移，利用其中的疾病信息在师生框架下实现单导联心电信号的诊断，实验证实该方法有出色的性能。

MVKT-ECG：多视角知识转移下的多标签心律失常高效单导联分类

MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring

Chest X-ray images are one of the most common medical diagnosis techniques to
identify different thoracic diseases. However, identification of pathologies in
X-ray images requires skilled manpower and are often cited as a time-consuming
task with varied level of interpretation, particularly in cases where the
identification of disease only by images is difficult for human eyes. With
recent achievements of deep learning in image classification, its application
in disease diagnosis has been widely explored. This research project presents a
multi-label disease diagnosis model of chest x-rays. Using Dense Convolutional
Neural Network (DenseNet), the diagnosis system was able to obtain high
classification predictions. The model obtained the highest AUC score of 0.896
for condition Cardiomegaly and the lowest AUC score for Nodule, 0.655. The
model also localized the parts of the chest radiograph that indicated the
presence of each pathology using GRADCAM, thus contributing to the model
interpretability of a deep learning algorithm.

本项目提出了一种基于 DenseNet 和 GRADCAM 的多标签胸透疾病诊断模型，能够在 X 线图像中准确检测出多种胸部病理，并实现了深度学习算法的模型可解释性。