ArticleJournal of medical systems2017
Endoscopic Image Classification and Retrieval using Clustered Convolutional Features.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology.Frontiers in plant science · 2022Pooled it
- Development of a Strategy to Predict and Detect Falls Using Wearable Sensors.Journal of medical systems · 2019Trial
- Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images.Animals : an open access journal from MDPI · 2025Article
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning.Diagnostics (Basel, Switzerland) · 2023Article
- Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation.Computational intelligence and neuroscience · 2022Article
- Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames.Journal of healthcare engineering · 2022Article
- GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases.PeerJ. Computer science · 2021Article
- Language-based translation and prediction of surgical navigation steps for endoscopic wayfinding assistance in minimally invasive surgery.International journal of computer assisted radiology and surgery · 2020Article
- Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions.Sensors (Basel, Switzerland) · 2020Article
- HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.Scientific data · 2020Article
- Medical Image Analysis using Convolutional Neural Networks: A Review.Journal of medical systems · 2018Review
- Medical Image Retrieval with Compact Binary Codes Generated in Frequency Domain Using Highly Reactive Convolutional Features.Journal of medical systems · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the growing use of minimally invasive surgical procedures, endoscopic video archives are growing at a rapid pace. Efficient access to relevant content in such huge multimedia archives require compact and discriminative visual features for indexing and matching. In this paper, we present an effective method to represent images using salient convolutional features. Convolutional kernels from the first layer of a pre-trained convolutional neural network (CNN) are analyzed and clustered into multiple distinct groups, based on their sensitivity to colors and textures. Dominant features detected by each cluster are collected into a single, layout-preserving feature map using a spatial maximal activator pooling (SMAP) approach. A moving window based structured pooling method then captures spatial layout features and global shape information from the aggregated feature map to populate feature histograms. Finally, individual histograms for each cluster are combined into a single comprehensive feature histogram. Clustering convolutional feature space allow extraction of color and texture features of varying strengths. Further, the SMAP approach enable us to select dominant discriminative features. The proposed features are compact and capable of conveniently outperforming several existing features extraction approaches in retrieval and classification tasks on endoscopy images dataset.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.