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Tissue-Specific labelling using Temporal Feature Extraction in Photoacoustic Remote Sensing Microscopy

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Tissue-Specific Labelling using Temporal Feature Extraction ...

Clinical and Translational Biophotonics, Translational 2022

作者： Tweel, James Pellegrino, Nicholas Ecclestone, Benjamin R. Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Vision and Image Processing Lab Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Illumisonics Inc. 22 King Street South WaterlooONN2J 1N8 Canada

Characteristic time-domain (TD) features are extracted from Photoacoustic Remote Sensing TD modulation using a novel temporal clustering algorithm. Features correspond to tissue-specific structures in unprocessed rese... 详细信息

ISBN: (纸本)9781557528209

关键词： Tissue

Text Detection & Recognition in the Wild for Robot Localization

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arXiv 2022年

作者： Raisi, Zobeir Zelek, John Vision and Image Processing Lab Systems Design Engineering Department University of Waterloo WaterlooONN2L 3G1 Canada

Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection & recognition in the wild is challenging due such factors as pose, irregular text, illumination and occlusion. We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. This model is more suitable for VPR. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. To evaluate our proposed architecture’s performance for VPR, we conducted several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The initial experimental results show that the proposed method outperforms the SOTA methods in terms of precision and recall when tested on this benchmark. © 2022, CC BY-NC-SA.

关键词： Robot applications

COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT images

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arXiv 2022年

作者： Gunraj, Hayden Tuinstra, Tia Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

Computed tomography (CT) has been widely explored as a COVID-19 screening and assessment tool to complement RT-PCR testing. To assist radiologists with CT-based COVID-19 screening, a number of computer-aided systems have been proposed. However, many proposed systems are built using CT data which is limited in both quantity and diversity. Motivated to support efforts in the development of machine learning-driven screening systems, we introduce COVIDx CT-3, a large-scale multinational benchmark dataset for detection of COVID-19 cases from chest CT images. COVIDx CT-3 includes 431,205 CT slices from 6,068 patients across at least 17 countries, which to the best of our knowledge represents the largest, most diverse dataset of COVID-19 CT images in open-access form. Additionally, we examine the data diversity and potential biases of the COVIDx CT-3 dataset, finding that significant geographic and class imbalances remain despite efforts to curate data from a wide variety of sources. Copyright © 2022, The Authors. All rights reserved.

关键词： Large dataset

Pixel-Level Sea Ice Concentration Retrieval from Sentinel-1 SAR imagery and Ancillary Data via Regional Loss Representations

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Pixel-Level Sea Ice Concentration Retrieval from Sentinel-1 ...

OCEANS

作者： Xinwei Chen Muhammed Patel Linlin Xu Yuhao Chen David A. Clausi K. Andrea Scott Weimin Huang School of Marine Science and Engineering South China University of Technology Department of System Design Engineering Vision and Image Processing Lab University of Waterloo Department of Mechanical & Mechatronics Engineering University of Waterloo Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

The automated monitoring and high resolution mapping of Arctic sea ice concentration (SIC) from synthetic aperture radar (SAR) images are crucial for various purposes, such as tactical navigation. However, the available labels for training deep learning-based SIC estimation models are mostly in coarse spatial resolution. This study tackles this key issue by proposing a novel loss calculation algorithm during model training. Consequently, a U-Net-based model can produce fine-grained SIC estimates via direct learning from ice charts in low resolution. The proposed method is evaluated with the recently release AI4Arctic Sea Ice Challenge Dataset with more than 500 Sentinel-1 SAR imager and associated ice charts. Visual interpretation and numerical results demonstrate the outstanding performance of the proposed method in terms of SIC mapping resolution. The effectiveness of incorporating multi-source ancillary data in performance improvement is also validated.

关键词： Training Visualization Sentinel-1 Estimation Radar polarimetry Numerical models Spatial resolution

Virtual Histology with Photon Absorption Remote Sensing using a Cycle-Consistent Generative Adversarial Network with Weakly Registered Pairs

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arXiv 2023年

作者： Tweel, James E.D. Ecclestone, Benjamin R. Boktor, Marian Simmons, James Alexander Tummon Fieguth, Paul Reza, Parsin Haji PhotoMedicine Labs Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada IllumiSonics Inc. 22 King Street South Suite 300 WaterlooONN2J 1N8 Canada Vision and Image Processing Lab Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Modern histopathology relies on the microscopic examination of thin tissue sections stained with histochemical techniques, typically using brightfield or fluorescence microscopy. However, the staining of samples can permanently alter their chemistry and structure, meaning an individual tissue section must be prepared for each desired staining contrast. This not only consumes valuable tissue samples but also introduces delays in essential diagnostic timelines. In this work, virtual histochemical staining is developed using label-free photon absorption remote sensing (PARS) microscopy. We present a method that generates virtually stained histology images that are indistinguishable from the gold standard hematoxylin and eosin (H&E) staining. First, PARS label-free ultraviolet absorption images are captured directly within unstained tissue specimens. The radiative and non-radiative absorption images are then preprocessed, and virtually stained through the presented pathway. The preprocessing pipeline features a self-supervised Noise2Void denoising convolutional neural network (CNN) as well as a novel algorithm for pixel-level mechanical scanning error correction. These developments significantly enhance the recovery of sub-micron tissue structures, such as nucleoli location and chromatin distribution. Finally, we used a cycle-consistent generative adversarial network CycleGAN architecture to virtually stain the preprocessed PARS data. Virtual staining is applied to thin unstained sections of malignant human skin and breast tissue samples. Clinically relevant details are revealed, with comparable contrast and quality to gold standard H&E-stained images. This work represents a crucial step to deploying label-free microscopy as an alternative to standard histopathology techniques. © 2023, CC BY.

关键词： Fluorescence microscopy

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray images for Computer-Aided COVID-19 Diagnostics

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arXiv 2022年

作者： Pavlova, Maya Tuinstra, Tia Aboutalebi, Hossein Zhao, Andy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Cheriton School of Computer Science University of Waterloo Canada Waterloo AI Institute University of Waterloo Canada DarwinAI Corp Canada

After more than two years since the beginning of the COVID-19 pandemic, the pressure of this crisis continues to devastate globally. The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is not only prevailing but has greatly increased due to its routine clinical use for respiratory complaints. Thus far, many visual perception models have been proposed for COVID-19 screening based on CXR imaging. Nevertheless, the accuracy and the generalization capacity of these models are very much dependent on the diversity and the size of the dataset they were trained on. Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 is composed of 30,386 CXR images from a multinational cohort of 17,026 patients from at least 51 countries, making it, to the best of our knowledge, the most extensive, most diverse COVID-19 CXR dataset in open access form. Here, we provide comprehensive details on the various aspects of the proposed dataset including patient demographics, imaging views, and infection types. The hope is that COVIDx CXR-3 can assist scientists in advancing machine learning research against both the COVID-19 pandemic and related diseases. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Text Detection and Recognition in the Wild: A Review

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arXiv 2020年

作者： Raisi, Zobeir Naiel, Mohamed A. Fieguth, Paul Wardell, Steven Zelek, John Vision and Image Processing Lab and Department of Systems Design Engineering University of Waterloo ONN2L 3G1 Canada ATS Automation Tooling Systems Inc. CambridgeONN3H 4R7 Canada

Detection and recognition of text in natural im-ages are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assessespre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques. Second, identifying several existing challenges for detecting or recognizing text in the wild images, namely, in-plane-rotation, multi-oriented and multi-resolution text, perspective distortion, illumination reflection, partial occlusion, complex fonts, and special characters. Finally, the paper also presents insight into the potential research directions in this field to address some of the mentioned challenges that are still encountering scene text detection and recognition techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

Do explanations reflect decisions? A machine-centric strategy to quantify the performance of explainability algorithms

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arXiv 2019年

作者： Lin, Zhong Qiu Shafiee, Mohammad Javad Bochkarev, Stanislav Jules, Michael St. Wang, Xiao Yu Wong, Alexander Vision & Image Processing Group Systems Design Engineering University of Waterloo Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

There has been a significant surge of interest recently in the research community around the concept of explainable artificial intelligence (XAI), where the goal is to produce an interpretation for a decision made by a machine learning algorithm. Of particular interest is the interpretation of how deep neural networks make decisions, given the complexity and 'black box' nature of such networks. Given the infancy of the field, there has been very limited exploration into the assessment of the performance of explainability methods, with most evaluations centered around subjective visual interpretation of the produced interpretations. In this study, we explore a more machine-centric strategy for quantifying the performance of explainability methods on deep neural networks via the notion of decision-making impact analysis. More specifically, we quantify the importance of identified critical factors for a given decision made by a network based on the impact over network decisions and confidences in the absence of these critical factors. For scenarios where we wish to study impact in directed erroneous decisions (e.g., under adversarial distractions), we additionally quantify importance of identified critical factors based on coverage of the adversarially impacted factors. We introduce two quantitative performance metrics: i) Impact Score, which assesses the percentage of critical factors with either strong confidence reduction impact or decision changing impact, and ii) Impact Coverage, which assesses the percentage coverage of adversarially impacted factors in the input. A comprehensive analysis using this approach was conducted on several state-of-the-art explainability methods (LIME, SHAP, Expected Gradients, GSInquire) on a ResNet-50 deep convolutional neural network using a subset of imageNet for the task of image classification. Experimental results show that, for both general and adversarial distraction scenarios, the critical regions identified by LIME within the

关键词： Deep neural networks

CACAI: Comparative analysis of crypto apps in India

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AIP Conference Proceedings 2023年第1期2916卷

作者： Rahul Johari Anuj Talwar Suhas Harbola Jyotsna Yadav Himesh Nayak Deo Prakash Vidyarthi SWINGER: Security Wireless IoT Network Group of Engineering and Research University School of Information Communication and Technology (USICT) Guru Gobind Singh Indraprastha University Sector-16C Dwarka Delhi India Computer vision and Image processing lab University School of Information Communication and Technology (USICT) Guru Gobind Singh Indraprastha University Sector-16C Dwarka Delhi India School of Computer and Systems Sciences Parallel and Distributed Systems Lab JNU Delhi India

Crypto-currency is a decentralized digital currency which can be used as a medium of exchange. As it does not have any central authority, it is unregulated and a volatile asset. Globally the crypto market is worth a few trillion dollars. The Indian crypto market suffered a lot due to a ban imposed by the Reserve Bank of India (RBI), an Indian regulatory agency, which lasted for around two years. Even after the ban and cautions issued by the RBI, the crypto market in India touched a billion dollar mark in no time. Currently, the crypto market has more than 10 million Indian investors which is growing fast as the number of exchange apps in the Indian market increase with the ease of trading in crypto. In the current research work Comparative Analysis of Crypto App in India (CACAI) has been performed and results exceed the expectations.

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