title: Scalable histopathological image analysis via supervised hashing with multiple features.
abstract: Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5 ms query time, comparing favorably with traditional methods.
authors: Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
authors: Menglin
authors: M
authors: Jiang
authors: Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. Electronic address: szhang16@uncc.edu.
authors: Shaoting
authors: S
authors: Zhang
authors: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.
authors: Junzhou
authors: J
authors: Huang
authors: Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors: Lin
authors: L
authors: Yang
authors: Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
authors: Dimitris N
authors: DN
authors: Metaxas
mesh_terms: D000465:Algorithms; D001940:Breast / Q000166:cytology / Q000000981:diagnostic imaging* / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D016208:Databases, Factual; D005260:Female; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Computer-aided diagnosis (CAD); Content-based image retrieval (CBIR); Feature fusion; Hashing; Histopathology