PDF(Pubmed)
Abstract：
UNASSIGNED: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn\'s disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements.
UNASSIGNED: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets.
UNASSIGNED: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61).
UNASSIGNED: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.
UNASSIGNED: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets.
UNASSIGNED: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61).
UNASSIGNED: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.
摘要：
■已发现标准深度学习方法不足以区分肠结核（ITB）和克罗恩病（CD）。这一缺点主要归因于可用样品的稀缺性。鉴于这种限制，我们的目标是开发一个创新的少射学习(FSL)系统，专门为CD和ITB的有效分类和鉴别诊断量身定制，使用内窥镜图像数据与最小的样品要求。
■总共收集了122张白光内窥镜图像（99张CD图像和23张ITB图像）（每位患者的回肠图像）。双向，设计了集成双重迁移学习和度量学习策略的3镜头FSL模型。选择Xception体系结构作为基础，然后使用来自HyperKvasir的食管炎图像进行双重转移过程。随后，从每个查询图像的Xception导出的特征向量被转换为预测分数，这是使用欧几里德距离从支持集中到六个参考图像计算的。
■FSL模型，利用双重迁移学习，在三轮评估中，与依赖单迁移学习的模型(AUC0.56)相比，表现出增强的性能指标(AUC0.81)。此外,它的表现超过了经验较少的内窥镜医师（AUC0.56），甚至超过了经验丰富的专家（AUC0.61）。
■我们开发的FSL模型证明了使用有限的内窥镜图像数据集区分CD和ITB的有效性。FSL对增强罕见疾病的诊断能力具有价值。
■总共收集了122张白光内窥镜图像（99张CD图像和23张ITB图像）（每位患者的回肠图像）。双向，设计了集成双重迁移学习和度量学习策略的3镜头FSL模型。选择Xception体系结构作为基础，然后使用来自HyperKvasir的食管炎图像进行双重转移过程。随后，从每个查询图像的Xception导出的特征向量被转换为预测分数，这是使用欧几里德距离从支持集中到六个参考图像计算的。
■FSL模型，利用双重迁移学习，在三轮评估中，与依赖单迁移学习的模型(AUC0.56)相比，表现出增强的性能指标(AUC0.81)。此外,它的表现超过了经验较少的内窥镜医师（AUC0.56），甚至超过了经验丰富的专家（AUC0.61）。
■我们开发的FSL模型证明了使用有限的内窥镜图像数据集区分CD和ITB的有效性。FSL对增强罕见疾病的诊断能力具有价值。
