no code implementations • 12 Apr 2024 • Mohammed Adnan, Qinle Ba, Nazim Shaikh, Shivam Kalra, Satarupa Mukherjee, Auranuch Lorsakul
In this work, we demonstrate that model pruning, as a model compression technique, can effectively reduce inference cost for computational and digital pathology based analysis with a negligible loss of analysis performance.
no code implementations • 7 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.
no code implementations • 29 Aug 2022 • Sobhan Hemati, Shivam Kalra, Morteza Babaie, H. R. Tizhoosh
Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task.
1 code implementation • 22 Nov 2021 • Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, Hamid R. Tizhoosh
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing.
no code implementations • 11 Jun 2021 • Shivam Kalra, Mohammed Adnan, Sobhan Hemati, Taher Dehkharghanian, Shahryar Rahnamayan, Hamid Tizhoosh
The feature extractor model is fine-tuned using hierarchical target labels of WSIs, i. e., anatomic site and primary diagnosis.
no code implementations • 15 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.
no code implementations • 8 Aug 2020 • Aditya Sriram, Shivam Kalra, Morteza Babaie, Brady Kieffer, Waddah Al Drobi, Shahryar Rahnamayan, Hany Kashani, Hamid. R. Tizhoosh
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.
no code implementations • 7 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.
no code implementations • 16 Apr 2020 • Mohammed Adnan, Shivam Kalra, Hamid. R. Tizhoosh
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology.
no code implementations • 20 Nov 2019 • Shivam Kalra, H. R. Tizhoosh, Sultaan Shah, Charles Choi, Savvas Damaskinos, Amir Safarpoor, Sobhan Shafiei, Morteza Babaie, Phedias Diamandis, Clinton JV Campbell, Liron Pantanowitz
The emergence of digital pathology has opened new horizons for histopathology and cytology.
1 code implementation • ECCV 2020 • Shivam Kalra, Mohammed Adnan, Graham Taylor, Hamid Tizhoosh
Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances.
no code implementations • 15 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.
no code implementations • 15 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.
no code implementations • 5 Mar 2019 • Shivam Kalra, Larry Li, Hamid. R. Tizhoosh
The results are encouraging in demonstrating the potential of machine learning methods for classification and encoding of pathology reports.
no code implementations • 3 May 2018 • Taha J. Alhindi, Shivam Kalra, Ka Hin Ng, Anika Afrin, Hamid. R. Tizhoosh
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.
no code implementations • 11 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.
no code implementations • 27 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.
no code implementations • 27 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.
no code implementations • 27 Sep 2017 • Aditya Sriram, Shivam Kalra, H. R. Tizhoosh, Shahryar Rahnamayan
Autoencoders have been recently used for encoding medical images.
no code implementations • 22 May 2017 • Morteza Babaie, Shivam Kalra, Aditya Sriram, Christopher Mitcheltree, Shujin Zhu, Amin Khatami, Shahryar Rahnamayan, H. R. Tizhoosh
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology.
no code implementations • 16 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).