A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
This work addresses the problem of creating trustworthy and data-efficient CBIR systems for medical imaging, specifically CT liver images, but it is incremental as it builds on existing self-supervised methods.
The paper tackled the limitations of deep learning-based content-based image retrieval (CBIR) for CT liver images, such as reliance on labeled data and lack of explainability, by proposing a self-supervised learning framework with domain-knowledge integration and conducting an explainability analysis, resulting in improved performance and generalization across datasets.
Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers 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) 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 generalisation 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.