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Found 19 papers, 1 papers with code
Forming Local Intersections of Projections for Classifying and Searching Histopathology Images
In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images.
A new Local Radon Descriptor for Content-Based Image Search
Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems.
Representation Learning of Histopathology Images using Graph Neural Networks
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology.
Automatic Classification of Pathology Reports using TF-IDF Features
The results are encouraging in demonstrating the potential of machine learning methods for classification and encoding of pathology reports.
Comparing LBP, HOG and Deep Features for Classification of Histopathology Images
In the present study, comparison of three classification models is conducted using features extracted using local binary patterns, the histogram of gradients, and a pre-trained deep network.
Opposition based Ensemble Micro Differential Evolution
Differential evolution (DE) algorithm with a small population size is called Micro-DE (MDE).
Tumour Ellipsification in Ultrasound Images for Treatment Prediction in Breast Cancer
One of the earliest steps in using QUS methods is contouring a region of interest (ROI) inside the tumour in ultrasound B-mode images.
Barcodes for Medical Image Retrieval Using Autoencoded Radon Transform
Using images in a training dataset, we autoencode Radon projections to perform binarization on outputs of hidden layers.
Automated Resolution Selection for Image Segmentation
This paper introduces a framework for the automated selection of the best resolution for image segmentation.
Gabor Barcodes for Medical Image Retrieval
This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side.
Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders
In this study, we explored using a deep de-noising autoencoder (DDA), with a new unsupervised training scheme using only backpropagation and dropout, to hash images into binary codes.
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform
The present work introduces a new image retrieval method for medical applications that employs a convolutional neural network (CNN) with recently introduced Radon barcodes.
Evolutionary Projection Selection for Radon Barcodes
A small number of equidistant projections, e. g., 4 or 8, is generally used to generate short barcodes.
Anatomy-Aware Measurement of Segmentation Accuracy
Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems.
Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer
Quantitative ultrasound (QUS) methods provide a promising framework that can non-invasively and inexpensively be used to predict or assess the tumour response to cancer treatment.
Diversity Enhancement for Micro-Differential Evolution
Furthermore, comprehensive comparative simulations and analysis on performance of the MDE algorithms over various mutation schemes, population sizes, problem types (i. e. uni-modal, multi-modal, and composite), problem dimensionalities, and mutation factor ranges are conducted by considering population diversity analysis for stagnation and trapping in local optimum situations.
Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types.
Evolving Fuzzy Image Segmentation with Self-Configuration
However, EFIS suffers from a few limitations when used in practice mainly due to some fixed parameters.
Learning Opposites with Evolving Rules
This, of course, is a very naive estimate of the actual or true (non-linear) opposite $\breve{x}_{II}$, which has been called type-II opposite in literature.
