Search Results for author: Shivam Kalra

Found 21 papers, 2 papers with code

Structured Model Pruning for Efficient Inference in Computational Pathology

no code implementations12 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.

Instance Segmentation Model Compression +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

Decentralized Federated Learning through Proxy Model Sharing

1 code implementation22 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.

Federated Learning 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

Forming Local Intersections of Projections for Classifying and Searching Histopathology Images

no code implementations8 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.

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.

Representation Learning of Histopathology Images using Graph Neural Networks

no code implementations16 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.

Representation Learning whole slide images

Learning Permutation Invariant Representations using Memory Networks

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.

3D Object Detection Classification +5

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.

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.

Automatic Classification of Pathology Reports using TF-IDF Features

no code implementations5 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.

Classification General Classification +1

Comparing LBP, HOG and Deep Features for Classification of Histopathology Images

no code implementations3 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.

Classification Image Classification

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

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

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

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