This paper presents a method to efficiently classify the gastroenterologic
section of images derived from Video Capsule Endoscopy (VCE) studies by
exploring the combination of a Convolutional Neural Network (CNN) for
classification with the time-series analysis properties of a Hidden Markov
Model (HMM). It is demonstrated that successive time-series analysis identifies
and corrects errors in the CNN output. Our approach achieves an accuracy of
$98.04\%$ on the Rhode Island (RI) Gastroenterology dataset. This allows for
precise localization within the gastrointestinal (GI) tract while requiring
only approximately 1M parameters and thus, provides a method suitable for low
power devices

本研究提出了一种有效地对视频胶囊内镜图像进行肠胃分类的方法，该方法通过将卷积神经网络（CNN）用于分类与隐马尔可夫模型（HMM）的时间序列分析属性相结合，演示了连续的时间序列分析可以识别和纠正 CNN 输出的错误，该方法在罗得岛（RI）胃肠病学数据集上达到了 98.04％的准确率，适用于只需大约 1M 参数的低功耗设备，从而可以精确定位胃肠道。

通过结合 CNNs 的分类和 HMMs 的时间序列分析在胃肠道中进行精确定位

Precise localization within the GI tract by combining classification of  CNNs and time-series analysis of HMMs

Introduction: Technical burdens and time-intensive review processes limit the
practical utility of video capsule endoscopy (VCE). Artificial intelligence
(AI) is poised to address these limitations, but the intersection of AI and VCE
reveals challenges that must first be overcome. We identified five challenges
to address. Challenge #1: VCE data are stochastic and contains significant
artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE
data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are
computationally cumbersome. Challenge #5: Clinicians are hesitant to accept
AIMLT that cannot explain their process.
Methods: An anatomic landmark detection model was used to test the
application of convolutional neural networks (CNNs) to the task of classifying
VCE data. We also created a tool that assists in expert annotation of VCE data.
We then created more elaborate models using different approaches including a
multi-frame approach, a CNN based on graph representation, and a few-shot
approach based on meta-learning.
Results: When used on full-length VCE footage, CNNs accurately identified
anatomic landmarks (99.1%), with gradient weighted-class activation mapping
showing the parts of each frame that the CNN used to make its decision. The
graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of
91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity
90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity
94.8%) performed well. Discussion: Each of these five challenges is addressed,
in part, by one of our AI-based models. Our goal of producing high performance
using lightweight models that aim to improve clinician confidence was achieved.

研究介绍了视频胶囊内窥镜、人工智能以及 VCE 数据解释等主题，使用卷积神经网络准确识别解剖标记，以轻量级模型提高临床医生信心。

胶囊内镜和人工智能的交叉领域：利用机器学习应对独特挑战

The intersection of video capsule endoscopy and artificial intelligence:  addressing unique challenges using machine learning

In the realm of modern diagnostic technology, video capsule endoscopy (VCE)
is a standout for its high efficacy and non-invasive nature in diagnosing
various gastrointestinal (GI) conditions, including obscure bleeding.
Importantly, for the successful diagnosis and treatment of these conditions,
accurate recognition of bleeding regions in VCE images is crucial. While deep
learning-based methods have emerged as powerful tools for the automated
analysis of VCE images, they often demand large training datasets with
comprehensive annotations. Acquiring these labeled datasets tends to be
time-consuming, costly, and requires significant domain expertise. To mitigate
this issue, we have embraced a semi-supervised learning (SSL) approach for the
bleeding regions segmentation within VCE. By adopting the `Mean Teacher'
method, we construct a student U-Net equipped with an scSE attention block,
alongside a teacher model of the same architecture. These models' parameters
are alternately updated throughout the training process. We use the
Kvasir-Capsule dataset for our experiments, which encompasses various GI
bleeding conditions. Notably, we develop the segmentation annotations for this
dataset ourselves. The findings from our experiments endorse the efficacy of
the SSL-based segmentation strategy, demonstrating its capacity to reduce
reliance on large volumes of annotations for model training, without
compromising on the accuracy of identification.

通过采用半监督学习方法和深度学习技术，我们开发了一种基于视频胶囊内窥镜图像的出血区域分割策略，有效减少了对大量标注数据的依赖，同时保持了准确识别的能力。