2.6k citations · 3.1k across the 13 of their papers we have counts for
13 papers
Cytoplasmic Strings Analysis in Human Embryo Time-Lapse Videos using Deep Learning Framework
Anabia Sohail, Mohamad Alansari, Ahmed Abughali +8
Infertility is a major global health issue, and while in-vitro fertilization has improved treatment outcomes, embryo selection remains a critical bottleneck. Time-lapse imaging ena…
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi +6
In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a sing…
Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review
Ahmad Obeid, Said Boumaraf, Anabia Sohail +5
Recent years witnessed remarkable progress in computational histopathology, largely fueled by deep learning. This brought the clinical adoption of deep learning-based tools within…
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
Asifullah Khan, Anabia Sohail, Mustansar Fiaz +11
Advances in deep learning are re-defining how visual data is processed and understand by the machines. Vision Transformers (ViTs) have recently demonstrated prominent performance i…
A survey of the Vision Transformers and their CNN-Transformer based Variants
Asifullah Khan, Zunaira Rauf, Anabia Sohail +4
Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with the…
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images
Momina Liaqat Ali, Zunaira Rauf, Asifullah Khan +3
Transformers, due to their ability to learn long range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning. Therefo…
Deep Neural Networks based Meta-Learning for Network Intrusion Detection
Anabia Sohail, Bibi Ayisha, Irfan Hameed +3
The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection s…
A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
Asifullah Khan, Saddam Hussain Khan, Mahrukh Saif +3
The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastat…
COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN
Saddam Hussain Khan, Anabia Sohail, Asifullah Khan
COVID-19 is a highly contagious respiratory infection that has affected a large population across the world and continues with its devastating consequences. It is imperative to det…
Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networks
Saddam Hussain Khan, Anabia Sohail, Asifullah Khan +1
COVID-19 is a global health problem. Consequently, early detection and analysis of the infection patterns are crucial for controlling infection spread as well as devising a treatme…
Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images
Anabia Sohail, Muhammad Ahsan Mukhtar, Asifullah Khan +3
Empirical evaluation of breast tissue biopsies for mitotic nuclei detection is considered an important prognostic biomarker in tumor grading and cancer progression. However, automa…
A Survey of the Recent Architectures of Deep Convolutional Neural Networks
Asifullah Khan, Anabia Sohail, Umme Zahoora +1
Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Pro…
A New Channel Boosted Convolutional Neural Network using Transfer Learning
Asifullah Khan, Anabia Sohail, Amna Ali
We present a novel architectural enhancement of Channel Boosting in a deep convolutional neural network (CNN). This idea of Channel Boosting exploits both the channel dimension of…