Search Results for author: H. R. Tizhoosh

Found 62 papers, 8 papers with code

Foundation Models and Information Retrieval in Digital Pathology

no code implementations13 Mar 2024 H. R. Tizhoosh

The paper reviews the state-of-the-art of foundation models, LLMs, generative AI, information retrieval and CBIR in digital pathology

Information Retrieval Retrieval

On Image Search in Histopathology

no code implementations14 Jan 2024 H. R. Tizhoosh, Liron Pantanowitz

Pathology images of histopathology can be acquired from camera-mounted microscopes or whole slide scanners.

Image Retrieval

Analysis and Validation of Image Search Engines in Histopathology

no code implementations6 Jan 2024 Isaiah Lahr, Saghir Alfasly, Peyman Nejat, Jibran Khan, Luke Kottom, Vaishnavi Kumbhar, Areej Alsaafin, Abubakr Shafique, Sobhan Hemati, Ghazal Alabtah, Nneka Comfere, Dennis Murphee, Aaron Mangold, Saba Yasir, Chady Meroueh, Lisa Boardman, Vijay H. Shah, Joaquin J. Garcia, H. R. Tizhoosh

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.

Image Retrieval whole slide images

Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection

no code implementations4 Oct 2023 Peyman Nejat, Areej Alsaafin, Ghazal Alabtah, Nneka Comfere, Aaron Mangold, Dennis Murphree, Patricija Zot, Saba Yasir, Joaquin J. Garcia, H. R. Tizhoosh

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.

Anomaly Detection whole slide images

When is a Foundation Model a Foundation Model

no code implementations14 Sep 2023 Saghir Alfasly, Peyman Nejat, Sobhan Hemati, Jibran Khan, Isaiah Lahr, Areej Alsaafin, Abubakr Shafique, Nneka Comfere, Dennis Murphree, Chady Meroueh, Saba Yasir, Aaron Mangold, Lisa Boardman, Vijay Shah, Joaquin J. Garcia, H. R. Tizhoosh

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.


Composite Biomarker Image for Advanced Visualization in Histopathology

no code implementations24 Apr 2023 Abubakr Shafique, Morteza Babaie, Ricardo Gonzalez, Adrian Batten, Soma Sikdar, H. R. Tizhoosh

In the first step, IHC biomarker images are aligned with the H&E images using one coordinate system and orientation.

whole slide images

Immunohistochemistry Biomarkers-Guided Image Search for Histopathology

no code implementations24 Apr 2023 Abubakr Shafique, Morteza Babaie, Ricardo Gonzalez, H. R. Tizhoosh

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.

Image Retrieval

Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

no code implementations15 Apr 2023 Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan, H. R. Tizhoosh

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.

Image Retrieval Representation Learning +1

Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'

no code implementations7 Apr 2023 Milad Sikaroudi, Mehdi Afshari, Abubakr Shafique, Shivam Kalra, H. R. Tizhoosh

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.

Binarization Image Retrieval +1

Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images

no code implementations21 Aug 2022 S. Maryam Hosseini, Milad Sikaroudi, Morteza Babaei, H. R. Tizhoosh

Finally, the central server aggregates the results, retrieving the average of models' weights and updating the model without having access to individual hospitals' weights.

Federated Learning Privacy Preserving

Monitoring Shortcut Learning using Mutual Information

no code implementations27 Jun 2022 Mohammed Adnan, Yani Ioannou, Chuan-Yung Tsai, Angus Galloway, H. R. Tizhoosh, Graham W. Taylor

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.

Autonomous Vehicles

Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification

no code implementations26 Mar 2022 Amir Safarpoor, Jason D. Hipp, H. R. Tizhoosh

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.

Multiple Instance Learning whole slide images

Gram Barcodes for Histopathology Tissue Texture Retrieval

1 code implementation28 Nov 2021 Shalev Lifshitz, Abtin Riasatian, H. R. Tizhoosh

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.

Image Retrieval Retrieval

Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing

no code implementations1 Oct 2021 Sobhan Hemati, H. R. Tizhoosh

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

no code implementations4 Aug 2021 Nitish Bhatt, David Ramon Prados, Nedim Hodzic, Christos Karanassios, H. R. Tizhoosh

External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radiological Technology datasets, respectively.

Decoder Generative Adversarial Network +2

A Similarity Measure of Histopathology Images by Deep Embeddings

no code implementations29 Jul 2021 Mehdi Afshari, H. R. Tizhoosh

The proposed similarity measure is an expansion of cosine vector similarity to a matrix.

Image Retrieval

Automatic Multi-Stain Registration of Whole Slide Images in Histopathology

no code implementations29 Jul 2021 Abubakr Shafique, Morteza Babaie, Mahjabin Sajadi, Adrian Batten, Soma Skdar, H. R. Tizhoosh

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.

Translation whole slide images

Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings

no code implementations15 Feb 2021 Sobhan Shafiei, Morteza Babaie, Shivam Kalra, H. R. Tizhoosh

The Kimia Path24 dataset has been introduced as a classification and retrieval dataset for digital pathology.

Image Retrieval Retrieval

Ink Marker Segmentation in Histopathology Images Using Deep Learning

no code implementations29 Oct 2020 Danial Maleki, Mehdi Afshari, Morteza Babaie, H. R. Tizhoosh

The quality of the images can be negatively affected when the glass slides are ink-marked by pathologists to delineate regions of interest.

Cross-Modal Retrieval whole slide images

Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem

1 code implementation10 Jul 2020 Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, H. R. Tizhoosh

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

Recognizing Magnification Levels in Microscopic Snapshots

no code implementations7 May 2020 Manit Zaveri, Shivam Kalra, Morteza Babaie, Sultaan Shah, Savvas Damskinos, Hany Kashani, H. R. Tizhoosh

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

1 code implementation5 Apr 2020 Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei, H. R. Tizhoosh, Fakhri Karray, Mark Crowley

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

1 code implementation4 Apr 2020 Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh, Fakhri Karray, Mark Crowley

We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights.

Dimensionality Reduction Face Recognition

Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Images

no code implementations20 Nov 2019 S. Kalra, C. Choi, S. Shah, L. Pantanowitz, H. R. Tizhoosh

With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention.

Image Retrieval Retrieval +1

Subtractive Perceptrons for Learning Images: A Preliminary Report

no code implementations15 Sep 2019 H. R. Tizhoosh, Shivam Kalra, Shalev Lifshitz, Morteza Babaie

In recent years, artificial neural networks have achieved tremendous success for many vision-based tasks.

Atrial Fibrillation Detection Using Deep Features and Convolutional Networks

no code implementations28 Mar 2019 Sara Ross-Howe, H. R. Tizhoosh

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.

Arrhythmia Detection Atrial Fibrillation Detection +1

Deep Features for Tissue-Fold Detection in Histopathology Images

no code implementations17 Mar 2019 Morteza Babaie, H. R. Tizhoosh

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

no code implementations17 Mar 2019 Wafa Chenni, Habib Herbi, Morteza Babaie, H. R. Tizhoosh

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).

Clustering Retrieval

Projectron -- A Shallow and Interpretable Network for Classifying Medical Images

no code implementations15 Mar 2019 Aditya Sriram, Shivam Kalra, H. R. Tizhoosh

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

no code implementations30 Apr 2018 Sara Ross-Howe, H. R. Tizhoosh

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.

Lesion Segmentation Skin Lesion Segmentation

Local Radon Descriptors for Image Search

no code implementations11 Oct 2017 Morteza Babaie, H. R. Tizhoosh, Amin Khatami, M. E. Shiri

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.

Medical Image Retrieval Retrieval

Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks

no code implementations11 Oct 2017 Brady Kieffer, Morteza Babaie, Shivam Kalra, H. R. Tizhoosh

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.

General Classification Image Classification +2

Fast Barcode Retrieval for Consensus Contouring

no code implementations28 Sep 2017 H. R. Tizhoosh, G. J. Czarnota

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.

Image Segmentation Medical Image Segmentation +2

Skin Lesion Segmentation: U-Nets versus Clustering

no code implementations27 Sep 2017 Bill S. Lin, Kevin Michael, Shivam Kalra, H. R. Tizhoosh

The first approach uses U-Nets and introduces a histogram equalization based preprocessing step.

Clustering Lesion Segmentation +2

A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images

no code implementations27 Sep 2017 Meghana Dinesh Kumar, Morteza Babaie, Shujin Zhu, Shivam Kalra, H. R. Tizhoosh

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 General Classification +1

Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives

no code implementations27 Sep 2017 Hamed Erfankhah, Mehran Yazdi, H. R. Tizhoosh

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.

Content-Based Image Retrieval General Classification +1

Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections

no code implementations2 Jan 2017 Morteza Babaie, H. R. Tizhoosh, Shujin Zhu, M. E. Shiri

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.

Content-Based Image Retrieval Retrieval

Stacked Autoencoders for Medical Image Search

no code implementations2 Oct 2016 S. Sharma, I. Umar, L. Ospina, D. Wong, H. R. Tizhoosh

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.

Content-Based Image Retrieval Medical Diagnosis +1

MinMax Radon Barcodes for Medical Image Retrieval

no code implementations2 Oct 2016 H. R. Tizhoosh, Shujin Zhu, Hanson Lo, Varun Chaudhari, Tahmid Mehdi

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.

Medical Image Retrieval Retrieval +1

Learning Opposites Using Neural Networks

no code implementations16 Sep 2016 Shivam Kalra, Aditya Sriram, Shahryar Rahnamayan, H. R. Tizhoosh

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).

Evolutionary Algorithms Vocal Bursts Type Prediction

Radon-Gabor Barcodes for Medical Image Retrieval

no code implementations16 Sep 2016 Mina Nouredanesh, H. R. Tizhoosh, Ershad Banijamali, James Tung

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.

Medical Image Retrieval Retrieval

Radon Features and Barcodes for Medical Image Retrieval via SVM

no code implementations16 Apr 2016 Shujin Zhu, H. R. Tizhoosh

To retrieve similar images when a query image is given, Radon projections and the barcode of the query image are generated.

Content-Based Image Retrieval Medical Image Retrieval +2

Self-Configuring and Evolving Fuzzy Image Thresholding

no code implementations15 Sep 2015 A. Othman, H. R. Tizhoosh, F. Khalvati

Every segmentation algorithm has parameters that need to be adjusted in order to achieve good results.

feature selection Image Segmentation +2

Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data

no code implementations15 Sep 2015 Zehra Camlica, H. R. Tizhoosh, Farzad Khalvati

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.

General Classification Image Classification +4

Autoencoding the Retrieval Relevance of Medical Images

no code implementations5 Jul 2015 Zehra Camlica, H. R. Tizhoosh, Farzad Khalvati

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.

Content-Based Image Retrieval Retrieval

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