[11], a modality feature-based re-ranking model is proposed for medical image retrieval based on medical image–dependent features. These features are manually selected by a medical expert from imaging modalities (e.g., image modality and image scale) and medical terminology.
May 7, 2018 · Therefore, we propose a novel reranking method based on medical-image-dependent features. These features are manually selected by a medical ...
May 7, 2018 · Therefore, we propose a novel reranking method based on medical-image-dependent features. These features are manually selected by a medical ...
However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results.
Oct 22, 2024 · Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical ...
MF-Re-Rank: A modality feature-based Re-Ranking model for medical image retrieval. Hajer Ayadi, Mouna Torjmen Khemakhem, Mariam Daoud, Jimmy Xiangji Huang ...
The findings suggest that integrating modality-specific features into the retrieval process can substantially enhance the efficiency and accuracy of medical ...
We define new medical-dependent query features such as image modality and presence of specific medical image terminology and make use of existing generic query ...
Nov 21, 2024 · MF-Re-Rank: A modality feature-based Re-Ranking model for medical image retrieval. Article. Jun 2016. Hajer Ayadi ...
Our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF).