Motion mode (M-mode) recording is an essential part of echocardiography to
measure cardiac dimension and function. However, the current diagnosis cannot
build an automatic scheme, as there are three fundamental obstructs: Firstly,
there is no open dataset available to build the automation for ensuring
constant results and bridging M-mode echocardiography with real-time instance
segmentation (RIS); Secondly, the examination is involving the time-consuming
manual labelling upon M-mode echocardiograms; Thirdly, as objects in
echocardiograms occupy a significant portion of pixels, the limited receptive
field in existing backbones (e.g., ResNet) composed from multiple convolution
layers are inefficient to cover the period of a valve movement. Existing
non-local attentions (NL) compromise being unable real-time with a high
computation overhead or losing information from a simplified version of the
non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode
echocardiography measurement scheme, contributes three aspects to answer the
problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance
segmentation, to enable consistent results and support the development of an
automatic scheme; 2) propose panel attention, local-to-global efficient
attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS
scheme toward big object detection with global receptive field; 3) develop and
implement AMEM, an efficient algorithm of automatic M-mode echocardiography
measurement enabling fast and accurate automatic labelling among diagnosis. The
experimental results show that RAMEM surpasses existing RIS backbones (with
non-local attention) in PASCAL 2012 SBD and human performances in real-time
MEIS tested. The code of MEIS and dataset are available at
this https URL

提出了 RAMEM，一种实时自动 M 型超声心动图测量方案，包括提供 MEIS 数据集、基于面板注意力的局部到全局高效注意力、以及快速准确的自动标注算法 AMEM，实验证明 RAMEM 在实时 MEIS 测试中超过现有 RIS 骨干网络（具有非局部注意力）和人类表现。

基于面板注意力的实时自动 M 模式心房超声测量

Real-time Automatic M-mode Echocardiography Measurement with Panel  Attention from Local-to-Global Pixels

Finding relevant and high-quality datasets to train machine learning models
is a major bottleneck for practitioners. Furthermore, to address ambitious
real-world use-cases there is usually the requirement that the data come
labelled with high-quality annotations that can facilitate the training of a
supervised model. Manually labelling data with high-quality labels is generally
a time-consuming and challenging task and often this turns out to be the
bottleneck in a machine learning project. Weak Supervised Learning (WSL)
approaches have been developed to alleviate the annotation burden by offering
an automatic way of assigning approximate labels (pseudo-labels) to unlabelled
data based on heuristics, distant supervision and knowledge bases. We apply
probabilistic generative latent variable models (PLVMs), trained on heuristic
labelling representations of the original dataset, as an accurate, fast and
cost-effective way to generate pseudo-labels. We show that the PLVMs achieve
state-of-the-art performance across four datasets. For example, they achieve
22% points higher F1 score than Snorkel in the class-imbalanced Spouse dataset.
PLVMs are plug-and-playable and are a drop-in replacement to existing WSL
frameworks (e.g. Snorkel) or they can be used as benchmark models for more
complicated algorithms, giving practitioners a compelling accuracy boost.

本研究提出了基于生成模型的自动标注方法，通过弱监督学习去减轻手动标注的负担，取得了比现有方法更好的性能表现，提高了机器学习项目的生产效率。