2.7k citations · 3.4k across the 32 of their papers we have counts for
33 papers
Video Relationship Detection Using Mixture of Experts
Ala Shaabana, Zahra Gharaee, Paul Fieguth
Machine comprehension of visual information from images and videos by neural networks faces two primary challenges. Firstly, there exists a computational and inference gap in conne…
Memory-Efficient Continual Learning Object Segmentation for Long Video
Amir Nazemi, Mohammad Javad Shafiee, Zahra Gharaee +1
Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have shown significant improvements in target object segmentation accuracy when information from pre…
Multi-Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing Images
Marian Boktor, James E. D. Tweel, Benjamin R. Ecclestone +3
Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective…
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
Zahra Gharaee, ZeMing Gong, Nicholas Pellegrino +13
In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is taxonomically classified by a…
Virtual Histology with Photon Absorption Remote Sensing using a Cycle-Consistent Generative Adversarial Network with Weakly Registered Pairs
James E. D. Tweel, Benjamin R. Ecclestone, Marian Boktor +3
Modern histopathology relies on the microscopic examination of thin tissue sections stained with histochemical techniques, typically using brightfield or fluorescence microscopy. H…
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?
Jinman Park, Francois Barnard, Saad Hossain +2
Unsupervised domain adaptation (UDA) aims to bridge the gap between source and target domains in the absence of target domain labels using two main techniques: input-level alignmen…
CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning
Amir Nazemi, Zeyad Moustafa, Paul Fieguth
Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the…
Machine Learning Challenges of Biological Factors in Insect Image Data
Nicholas Pellegrino, Zahra Gharaee, Paul Fieguth
The BIOSCAN project, led by the International Barcode of Life Consortium, seeks to study changes in biodiversity on a global scale. One component of the project is focused on study…
Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation
Jinman Park, Kimathi Kaai, Saad Hossain +3
Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camer…
Virtual Histological Staining of Label-Free Total Absorption Photoacoustic Remote Sensing (TA-PARS)
Marian Boktor, Benjamin Ecclestone, Vlad Pekar +4
Histopathological visualizations are a pillar of modern medicine and biological research. Surgical oncology relies exclusively on post-operative histology to determine definitive s…
Time-domain feature extraction for target-specificity in Photoacoustic Remote Sensing Microscopy
Nicholas Pellegrino, Benjamin R. Ecclestone, Paul Fieguth +1
Photoacoustic Remote Sensing (PARS) microscopy is an emerging label-free optical absorption imaging modality. PARS operates by capturing nanosecond-scale optical perturbations gene…
K-Means for Noise-Insensitive Multi-Dimensional Feature Learning
Nicholas Pellegrino, Paul Fieguth, Parsin Haji Reza
Many measurement modalities which perform imaging by probing an object pixel-by-pixel, such as via Photoacoustic Microscopy, produce a multi-dimensional feature (typically a time-d…
Survey of Deep Learning Methods for Inverse Problems
Shima Kamyab, Zohreh Azimifar, Rasool Sabzi +1
In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three catego…
In vivo functional and structural retina imaging using multimodal photoacoustic remote sensing microscopy and optical coherence tomography
Zohreh Hosseinaee, Nicholas Pellegrino, Nima Abbasi +4
We have developed a multimodal photoacoustic remote sensing (PARS) microscope combined with swept source optical coherence tomography for in vivo, non-contact retinal imaging. Buil…
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation
André M. Carrington, Douglas G. Manuel, Paul W. Fieguth +9
Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary cl…
Deep Learning for Instance Retrieval: A Survey
Wei Chen, Yu Liu, Weiping Wang +5
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of con…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…
Improving Maximal Safe Brain Tumor Resection with Photoacoustic Remote Sensing Microscopy
Benjamin R. Ecclestone, Kevan Bell, Saad Abbasi +5
Malignant brain tumors are among the deadliest neoplasms with the lowest survival rates of any cancer type. In considering surgical tumor resection, suboptimal extent of resection…
Text Detection and Recognition in the Wild: A Review
Zobeir Raisi, Mohamed A. Naiel, Paul Fieguth +2
Detection and recognition of text in natural images are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, aut…
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi +2
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Potential adversarial samples for white-box attacks
Amir Nazemi, Paul Fieguth
Deep convolutional neural networks can be highly vulnerable to small perturbations of their inputs, potentially a major issue or limitation on system robustness when using deep net…
Assessing Architectural Similarity in Populations of Deep Neural Networks
Audrey Chung, Paul Fieguth, Alexander Wong
Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures o…
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Xiaodan Hu, Audrey G. Chung, Paul Fieguth +3
Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Spec…
Mitigating Architectural Mismatch During the Evolutionary Synthesis of Deep Neural Networks
Audrey Chung, Paul Fieguth, Alexander Wong
Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the organic synthesis of increasingly efficient archite…