In medical imaging, accurate retrieval of relevant images is crucial for diagnosis and treatment. However, current image retrieval methods often struggle to match the desired images due to a gap between low-level features and high-level semantic concepts. This research proposes a novel approach that bridges this gap using relevance feedback (RF) to improve image retrieval accuracy.
Keywords: Content-based image retrieval (CBIR), Relevance feedback (RF), Semantic gap, Short-term learning, Long-term learning, Object detection, Feature extraction, Convolutional neural networks (CNNs).
Summary
The article discusses a novel approach to improving medical image retrieval by bridging the semantic gap between low-level features and high-level concepts. The proposed method utilizes relevance feedback (RF) to enhance the accuracy of image retrieval, which is essential for diagnosis and treatment. By leveraging RF, the method can refine the ranking criteria and retrieve more relevant images in subsequent iterations. This approach shows significant improvement in retrieval accuracy even in the initial attempt, demonstrating its potential to improve medical imaging diagnosis and treatment.
To understand this concept, imagine a library where books are organized based on their physical features (e.g., author, title, publisher). However, when a reader wants a specific book (e.g., Harry Potter), they may not find it easily due to the lack of a comprehensive cataloging system. By incorporating user feedback, the library can improve its organization and make it easier for readers to locate their desired books. Similarly, the proposed method enhances medical image retrieval by incorporating relevance feedback to bridge the semantic gap between low-level features and high-level concepts, leading to more accurate and efficient retrieval.
In summary, the article proposes a novel approach to improving medical image retrieval using relevance feedback to bridge the semantic gap between low-level features and high-level concepts. The proposed method demonstrates significant improvement in retrieval accuracy even in the initial attempt, making it an exciting development in the field of medical imaging diagnosis and treatment.
