Search Results for author:
Found 61 papers, 8 papers with code
Foundation Models and Information Retrieval in Digital Pathology
The paper reviews the state-of-the-art of foundation models, LLMs, generative AI, information retrieval and CBIR in digital pathology
Training Artificial Neural Networks by Coordinate Search Algorithm
Finding the optimal values for weights of ANNs is a large-scale optimization problem.
On Image Search in Histopathology
Pathology images of histopathology can be acquired from camera-mounted microscopes or whole slide scanners.
Analysis and Validation of Image Search Engines in Histopathology
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction.
Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images
We assess the representativeness of the SDM montage across various public and private histopathology datasets.
Rotation-Agnostic Image Representation Learning for Digital Pathology
This paper addresses complex challenges in histopathological image analysis through three key contributions.
Breast Cancer Histology Image Classification
Medical Image Retrieval
+2
Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection
While most of the computational pathology tasks are designed to classify or detect the presence of pathological lesions in each WSI, the confounding role and redundant nature of normal histology in tissue samples are generally overlooked in WSI representations.
When is a Foundation Model a Foundation Model
Recently, several studies have reported on the fine-tuning of foundation models for image-text modeling in the field of medicine, utilizing images from online data sources such as Twitter and PubMed.
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels.
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.
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.
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.
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology
By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label.
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering.
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
Coarse multi-objective feature selection uses the reduced search space strategy guided by the classification accuracy and the number of features.
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.
Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images
Finally, the central server aggregates the results, retrieving the average of models' weights and updating the model without having access to individual hospitals' weights.
Monitoring Shortcut Learning using Mutual Information
The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critical domains such as healthcare, finance and autonomous vehicles.
Hospital-Agnostic Image Representation Learning in Digital Pathology
These variabilities are assumed to cause a domain shift in the images of different hospitals.
Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification
In this paper, we propose tRNAsfomer, an attention-based topology that can learn both to predict the bulk RNA-seq from an image and represent the whole slide image of a glass slide simultaneously.
Gram Barcodes for Histopathology Tissue Texture Retrieval
Recent advances in digital pathology have led to the need for Histopathology Image Retrieval (HIR) systems that search through databases of biopsy images to find similar cases to a given query image.
Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing
We relax the orthogonality constraint on the projection in a PCA-formulation and regularize this by a quantization term.
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks
External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radiological Technology datasets, respectively.
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.
A Similarity Measure of Histopathology Images by Deep Embeddings
The proposed similarity measure is an expansion of cosine vector similarity to a matrix.
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.
Magnification Generalization for Histopathology Image Embedding
However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.
Breast Cancer Histology Image Classification
Classification Of Breast Cancer Histology Images
+3
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.
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
However, sampling from stochastic distributions of data rather than sampling merely from the existing embedding instances can provide more discriminative information.
Dimensionality Reduction
Histopathological Image Classification
+1
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100, 000 patches.
Dimensionality Reduction
Histopathological Image Classification
+1
A Comparative Study of U-Net Topologies for Background Removal in Histopathology Images
During the last decade, the digitization of pathology has gained considerable momentum.
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.
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.
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method.
Classification Of Breast Cancer Histology Images
Dimensionality Reduction
+3
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights.
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.
Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images
With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention.
Subtractive Perceptrons for Learning Images: A Preliminary Report
In recent years, artificial neural networks have achieved tremendous success for many vision-based tasks.
Atrial Fibrillation Detection Using Deep Features and Convolutional Networks
The first approach used a pretrained DenseNet model to extract features that were then classified using Support Vector Machines, and the second approach used the spectrograms as direct input into a convolutional network.
Ranked #6 on
Atrial Fibrillation Detection
on MIT-BIH AF
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).
Projectron -- A Shallow and Interpretable Network for Classifying Medical Images
This paper introduces the `Projectron' as a new neural network architecture that uses Radon projections to both classify and represent medical images.
The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation
Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined.
Auto-Detection of Safety Issues in Baby Products
Every year, thousands of people receive consumer product related injuries.
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.
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.
Fast Barcode Retrieval for Consensus Contouring
Because the accuracy of experts' contours must be measured, we first used 500 synthetic prostate images with their gold markers and delineations by 20 simulated users.
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.
Learning Autoencoded Radon Projections
Autoencoders have been recently used for encoding medical images.
Skin Lesion Segmentation: U-Nets versus Clustering
The first approach uses U-Nets and introduces a histogram equalization based preprocessing step.
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives
Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decision of the clinical experts and as well in educational scenarios.
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.
Stacked Autoencoders for Medical Image Search
The technique is applied to the IRMA dataset, a collection of 14, 410 x-ray images in order to demonstrate the ability of autoencoders to retrieve similar x-rays given test queries.
MinMax Radon Barcodes for Medical Image Retrieval
As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset.
Radon-Gabor Barcodes for Medical Image Retrieval
The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks.
Learning Opposites Using Neural Networks
In this paper, we introduce an approach to learn type-II opposites from the given inputs and their outputs using the artificial neural networks (ANNs).
Radon Features and Barcodes for Medical Image Retrieval via SVM
To retrieve similar images when a query image is given, Radon projections and the barcode of the query image are generated.
Self-Configuring and Evolving Fuzzy Image Thresholding
Every segmentation algorithm has parameters that need to be adjusted in order to achieve good results.
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified.
Autoencoding the Retrieval Relevance of Medical Images
Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data.
