A5050060540- AI in cancer detection
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Medical Image Segmentation Techniques
- Lung Cancer Diagnosis and Treatment
- Image Processing Techniques and Applications
- Digital Radiography and Breast Imaging
- Identification and Quantification in Food
- Brain Tumor Detection and Classification
- Advanced X-ray and CT Imaging
- Global Cancer Incidence and Screening
- Cell Image Analysis Techniques
- Cellular Mechanics and Interactions
- Advanced Fluorescence Microscopy Techniques
- Distributed and Parallel Computing Systems
- Image Retrieval and Classification Techniques
- Advanced MRI Techniques and Applications
- Food Supply Chain Traceability
- Diabetic Foot Ulcer Assessment and Management
- Health, Environment, Cognitive Aging
- Scientific Computing and Data Management
- Advanced Chemical Sensor Technologies
- Meat and Animal Product Quality
- Bee Products Chemical Analysis
Commonwealth Scientific and Industrial Research Organisation
2010-2025
Data61
2016-2025
Vision Australia
2024
National Imaging Facility
2020
Cancer Council Australia
2018
Health Sciences and Nutrition
2016
University Medical Center Utrecht
2007-2009
Utrecht University
2006-2009
Heidelberg University
2009
University Hospital Heidelberg
2009
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms common database. A collection of 20 clinical images with reference segmentations was provided to train tune in advance. Participants were also...
While Cumulus – a semi-automated method for measuring breast density is utilised extensively in research, it labour-intensive and unsuitable screening programmes that require an efficient valid measure on which to base recommendations. We develop automated (AutoDensity) compare terms of association with cancer risk outcomes. AutoDensity automatically identifies the area mammogram classifies similar way Cumulus, through fast, stand-alone Windows or Linux program. Our sample comprised 985...
Recent advances in image classification methods, along with the availability of associated tools, has seen their use become widespread many domains. This paper presents a novel application current approaches area emergency situation awareness. We discuss based on low level features as well methods built top pre-trained classifiers. The performance classifiers are assessed terms accuracy consideration to computational aspects given size database. Specifically, we investigate context bush fire...
A computer‐aided detection (CAD) system is presented for the localization of interstitial lesions in chest radiographs. The analyzes complete lung fields using a two‐class supervised pattern classification approach to distinguish between normal texture and affected by disease. Analysis done pixel‐wise produces probability map an image where each pixel assigned being abnormal. Interstitial are often subtle ill defined on x‐rays hence difficult detect, even expert radiologists. Therefore new,...
Pneumoconiosis is an incurable respiratory disease caused by long-term inhalation of respirable dust. Due to small pneumoconiosis incidence and restrictions on sharing patient data, the number available X-rays insufficient, which introduces significant challenges for training deep learning models. In this paper, we use both real synthetic radiographs train a cascaded machine framework automated detection pneumoconiosis, including based pixel classifier lung field segmentation,...
Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation lung appearance caused by progression and differing X-ray settings. While models have shown remarkable success segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for...
Purpose A system is presented for automated estimation of progression interstitial lung disease in serial thoracic CT scans. Methods The compares corresponding 2D axial sections from baseline and follow‐up scans concludes whether this pair represents regression, progression, or unchanged status. correspondence between achieved by intrapatient volumetric image registration. classification function trained with two different feature sets. Features the first set represent intensity distribution...
Microbial colony growth can serve as a useful readout in assays for studying complex genetic interactions or the effects of chemical compounds. Although computational tools acquiring quantitative measurements microbial colonies have been developed, their utility be compromised by inflexible input image requirements, non-trivial installation procedures, complicated operation. Here, we present Spotsizer software tool automated size images robotically arrayed colonies. features convenient...
Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation Ryan Lagerstrom, Yulia Arzhaeva, Leanne Bischof, Simon Haberle, Felicitas Hopf, David Lovell; A comparison of classification algorithms within the Classifynder pollen imaging system. AIP Conf. Proc. 9 October 2013; 1559 (1): 250–259. https://doi.org/10.1063/1.4825017 Download citation file: Ris (Zotero)...
In this paper we compare and combine two distinct pattern classification approaches to the automated detection of regions with interstitial abnormalities in frontal chest radiographs. Standard two-class classifiers recently developed one-class are considered. The problem is find best model normal class reject all objects that don't fit normality. This methodology was deal poorly balanced classes, it uses only from a well-sampled for training. may be an advantageous approach medical...
Cloud computing services offer highly reliable, scalable and efficient solutions with a large pool of easily accessible, virtualized resources. They are becoming an increasingly prevalent delivery model. We have developed cloud-based image analysis toolbox to provide wide user base easy access the software tools we over last decade. The is provided as service on Australian national cloud infrastructure. design implementation presented, including its architecture, key components some...
Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation Tomasz Bednarz, Piotr Szul, Yulia Arzhaeva, Dadong Wang, Neil Burdett, Alex Khassapov, Shiping Chen, Pascal Vallotton, Ryan Lagerstrom, Tim Gureyev, John Taylor; Biomedical image analysis processing in clouds. AIP Conf. Proc. 9 October 2013; 1559 (1): 77–79. https://doi.org/10.1063/1.4824998 Download citation...