The gut microbiome, crucial for human health, presents challenges in
analyzing its complex metaomic data due to high dimensionality and sparsity.
Traditional methods struggle to capture its intricate relationships. We
investigate graph neural networks (GNNs) for this task, aiming to derive
meaningful representations of individual gut microbiomes. Unlike methods
relying solely on taxa abundance, we directly leverage phylogenetic
relationships, in order to obtain a generalized encoder for taxa networks. The
representation learnt from the encoder are then used to train a model for
phenotype prediction such as Inflammatory Bowel Disease (IBD).

利用图神经网络从肠道微生物组中获得个体微生物组的有意义的表示，并用于预测炎症性肠病等表型。

肠道菌群代谢组学数据的图神经网络：初步研究

Graph Neural Networks for Gut Microbiome Metaomic data: A preliminary  work

Gastrointestinal endoscopy is a medical procedure that utilizes a flexible
tube equipped with a camera and other instruments to examine the digestive
tract. This minimally invasive technique allows for diagnosing and managing
various gastrointestinal conditions, including inflammatory bowel disease,
gastrointestinal bleeding, and colon cancer. The early detection and
identification of lesions in the upper gastrointestinal tract and the
identification of malignant polyps that may pose a risk of cancer development
are critical components of gastrointestinal endoscopy's diagnostic and
therapeutic applications. Therefore, enhancing the detection rates of
gastrointestinal disorders can significantly improve a patient's prognosis by
increasing the likelihood of timely medical intervention, which may prolong the
patient's lifespan and improve overall health outcomes. This paper presents a
novel Transformer-based deep neural network designed to perform multiple tasks
simultaneously, thereby enabling accurate identification of both upper
gastrointestinal tract lesions and colon polyps. Our approach proposes a unique
global context-aware module and leverages the powerful MiT backbone, along with
a feature alignment block, to enhance the network's representation capability.
This novel design leads to a significant improvement in performance across
various endoscopic diagnosis tasks. Extensive experiments demonstrate the
superior performance of our method compared to other state-of-the-art
approaches.

本研究提出了一种多任务利用全局上下文感知模块和特征对齐块的 Transformer-based 深度神经网络，可用于精确识别上消化道病变和肠息肉，实验证明其性能卓越。

UGCANet：一种统一的全局上下文感知基于 Transformer 的网络，具有特征对齐功能，用于内窥镜图像分析

UGCANet: A Unified Global Context-Aware Transformer-based Network with  Feature Alignment for Endoscopic Image Analysis

Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is
graded by endoscopists and this assessment is the basis for risk stratification
and therapy monitoring. Presently, endoscopic characterisation is largely
operator dependant leading to sometimes undesirable clinical outcomes for
patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which
is widely used but requires the reliable identification of subtle changes in
mucosal inflammation. Most existing deep learning classification methods cannot
detect these fine-grained changes which make UC grading such a challenging
task. In this work, we introduce a novel patch-level instance-group
discrimination with pretext-invariant representation learning (PLD-PIRL) for
self-supervised learning (SSL). Our experiments demonstrate both improved
accuracy and robustness compared to the baseline supervised network and several
state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised
classification our proposed PLD-PIRL obtained an improvement of 4.75% on
hold-out test data and 6.64% on unseen center test data for top-1 accuracy.

本研究提出了一种新的自我监督学习方法，即 patch-level instance-group discrimination with pretext-invariant representation learning (PLD-PIRL)，通过该方法可以更准确、更稳健地检测肠炎患者黏膜炎症细微的变化，相较于基准监督学习网络和现有的一些自我监督学习方法，本方法表现出更好的准确性和鲁棒性。