计算机科学
基于内容的图像检索
人工智能
背景(考古学)
领域(数学)
图像检索
可用性
特征提取
深度学习
过程(计算)
模式识别(心理学)
情报检索
机器学习
图像(数学)
人机交互
古生物学
数学
纯数学
生物
操作系统
作者
Kristoffer Wickstrøm,Eirik Agnalt Østmo,Keyur Radiya,Karl Øyvind Mikalsen,Michael Kampffmeyer,Robert Jenssen
标识
DOI:10.1016/j.compmedimag.2023.102239
摘要
Deep learning-based approaches for content-based image retrieval (CBIR) of computed tomography (CT) liver images is an active field of research, but suffer from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems. We address these limitations by: (1) Proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure, and, (2) by providing the first representation learning explainability analysis in the context of CBIR of CT liver images. Results demonstrate improved performance compared to the standard self-supervised approach across several metrics, as well as improved generalization across datasets. Further, we conduct the first representation learning explainability analysis in the context of CBIR, which reveals new insights into the feature extraction process. Lastly, we perform a case study with cross-examination CBIR that demonstrates the usability of our proposed framework. We believe that our proposed framework could play a vital role in creating trustworthy deep CBIR systems that can successfully take advantage of unlabeled data.
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