Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces
Explore this paper's citation graph
Summary
B boosted spectral embedding (BoSE) is presented, which utilizes a boosted distance metric to selectively weight individual features (based on training data) to subsequently map the data into a reduced-dimensional space and could serve as an important tool for CBIR and classification of high-dimensional biomedical data.
- Type
- article
- Published
- 2015-01-01
- Cited by
- 32
- References
- 40
- Access
- Open access
- OpenAlex
- https://openalex.org/W1933770199
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:36067648
Keywords
Computer science, Content-based image retrieval, Pattern recognition (psychology), Image retrieval, Histopathology
References
- Histological Grading and Prognosis in Breast Cancer
- Spectral embedding finds meaningful (relevant) structure in image and microarray data
- Adaptive Metrics for Content Based Image Retrieval in Dermatology
- On the Statistical Analysis of Dirty Pictures
- A kernel view of the dimensionality reduction of manifolds
- Retrieval and ranking of biomedical images using boosted haar features
- Digital pathology image analysis: opportunities and challenges
- A decision-theoretic generalization of on-line learning and an application to boosting
- A global geometric framework for nonlinear dimensionality reduction.
- A boosted distance metric: application to content based image retrieval and classification of digitized histopathology
- Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS
- A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies
- Textural Features for Image Classification
- Unsupervised texture segmentation using Gabor filters
- High-Throughput Detection of Prostate Cancer in Histological Sections Using Probabilistic Pairwise Markov Models
- Nonlinear dimensionality reduction by locally linear embedding.
- Inflammation and cancer, the mastocytoma P815 tumor model revisited: Triggering of macrophage activation in vivo with pro‐tumorigenic consequences
- Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data
- A Semantic Content-Based Retrieval Method for Histopathology Images
- A Riemannian approach to graph embedding
Cited by
- Evaluating stability of histomorphometric features across scanner and staining variations: predicting biochemical recurrence from prostate cancer whole slide images
- Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images
- Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images
- Exploring Mediatoil Imagery: A Content-based Approach
- Machine Learning Methods for Histopathological Image Analysis
- Large-Scale Annotation of Histopathology Images from Social Media
- Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools
- Similar image search for histopathology: SMILY
- Heterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images
- Patch Clustering for Representation of Histopathology Images
- Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning
- Machine learning approaches for pathologic diagnosis
- Development of orthotopic tumour models using ultrasound-guided intrahepatic injection
- Content-based histopathological image retrieval using multi-scale and multichannel decoder based LTP
- Visual Analytics in Digital Pathology: Challenges and Opportunities
- Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media
- Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends
- Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives
- A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach
- Multi-Magnification Image Search in Digital Pathology
Related papers
- CBIR using Speech, Text & Image Query for Mobile Device
- Uniform Extended Local Ternary Pattern for Content Based Image Retrieval
- A novel approach for content based image retrieval in context of combination S C techniques
- An exogenous approach for adding multiple image representations to content-based image retrieval systems
- A survey on content based image retrieval
- Localized Content-Based Image Retrieval
- An Efficient CNN-Based Method for Content-Based Image Retrieval