Search Results for author:
Found 21 papers, 1 papers with code
On Philomatics and Psychomatics for Combining Philosophy and Psychology with Mathematics
We enumerate various examples for philomatics and psychomatics, some of which are explained in more depth.
An Investigation into Glomeruli Detection in Kidney H&E and PAS Images using YOLO
Conclusions: Automated glomeruli detection in human kidney images is possible using modern AI models.
Composite Biomarker Image for Advanced Visualization in Histopathology
In the first step, IHC biomarker images are aligned with the H&E images using one coordinate system and orientation.
Immunohistochemistry Biomarkers-Guided Image Search for Histopathology
In this work, we are proposing a targeted image search approach, inspired by the pathologists workflow, which may use information from multiple IHC biomarker images when available.
Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task.
Automatic Multi-Stain Registration of Whole Slide Images in Histopathology
The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images.
Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings
The Kimia Path24 dataset has been introduced as a classification and retrieval dataset for digital pathology.
Ink Marker Segmentation in Histopathology Images Using Deep Learning
The quality of the images can be negatively affected when the glass slides are ink-marked by pathologists to delineate regions of interest.
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.
Recognizing Magnification Levels in Microscopic Snapshots
In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition.
Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence
The emergence of digital pathology has opened new horizons for histopathology and cytology.
Subtractive Perceptrons for Learning Images: A Preliminary Report
In recent years, artificial neural networks have achieved tremendous success for many vision-based tasks.
Deep Features for Tissue-Fold Detection in Histopathology Images
Whole slide imaging (WSI) refers to the digitization of a tissue specimen which enables pathologists to explore high-resolution images on a monitor rather than through a microscope.
Patch Clustering for Representation of Histopathology Images
The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e. g., indexing and search).
Deep Barcodes for Fast Retrieval of Histopathology Scans
We investigate the concept of deep barcodes and propose two methods to generate them in order to expedite the process of classification and retrieval of histopathology images.
Local Radon Descriptors for Image Search
This paper attempts to show that the dense sampling to generate the histogram of local Radon projections has a much higher discrimination capability than the global one.
Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks
We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks.
A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images
This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images.
Classification and Retrieval of Digital Pathology Scans: A New Dataset
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology.
Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections
Our method (Single Projection Radon Barcode, or SP-RBC) uses only a few Radon single projections for each image as global features that can serve as a basis for weak learners.
