A5085370896- AI in cancer detection
- Digital Radiography and Breast Imaging
- Radiomics and Machine Learning in Medical Imaging
- Global Cancer Incidence and Screening
- Breast Lesions and Carcinomas
- Medical Imaging Techniques and Applications
- Image Retrieval and Classification Techniques
- MRI in cancer diagnosis
- Medical Image Segmentation Techniques
- Breast Cancer Treatment Studies
- Colorectal Cancer Screening and Detection
- Digital Imaging for Blood Diseases
- Gene expression and cancer classification
- Radiology practices and education
- Advanced MRI Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- Infrared Thermography in Medicine
- Bayesian Modeling and Causal Inference
- Biomedical Text Mining and Ontologies
- Cell Image Analysis Techniques
- Advanced X-ray and CT Imaging
- BRCA gene mutations in cancer
- COVID-19 diagnosis using AI
- Ultrasound Imaging and Elastography
- Advanced Data Compression Techniques
Radboud University Nijmegen
2016-2025
Radboud University Medical Center
2016-2025
University of Copenhagen
2023
Gentofte Hospital
2023
University Medical Center
2003-2021
Maastricht University Medical Centre
2019
The Netherlands Cancer Institute
2019
Franciscus Vlietland
2019
Jeroen Bosch Ziekenhuis
2017
University of Manchester
2017
<h3>Importance</h3> Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. <h3>Objective</h3> Assess the performance automated at detecting metastases in hematoxylin eosin–stained tissue sections lymph nodes women with breast cancer compare it pathologists' diagnoses a setting. <h3>Design, Setting, Participants</h3> Researcher challenge competition (CAMELYON16) develop solutions for node (November 2015-November...
Extremely dense breast tissue is a risk factor for cancer and limits the detection of with mammography. Data are needed on use supplemental magnetic resonance imaging (MRI) to improve early reduce interval cancers in such patients.In this multicenter, randomized, controlled trial Netherlands, we assigned 40,373 women between ages 50 75 years extremely normal results screening mammography group that was invited undergo MRI or received only. The groups were 1:4 ratio, 8061 MRI-invitation...
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present method that learns feature hierarchy unlabeled data. When learned are used as input simple classifier, two different tasks can be addressed: i) density segmentation, ii) mammographic texture. The proposed model at multiple scales. To control models capacity novel sparsity regularizer is...
Variations in the color and intensity of hematoxylin eosin (H&E) stained histological slides can potentially hamper effectiveness quantitative image analysis. This paper presents a fully automated algorithm for standardization whole-slide histopathological images to reduce effect these variations. The proposed algorithm, called standardizer (WSICS), utilizes spatial information classify pixels into different stain components. chromatic density distributions each components...
Manual counting of mitotic tumor cells in tissue sections constitutes one the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect figures cancer based on convolutional neural networks (CNNs). Application CNNs hematoxylin eosin (H&E) stained histological hampered by: (1) noisy expensive reference standards established by pathologists, (2) lack generalization due staining variation across...
Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications MRI. Traditional image analysis computer vision techniques, such atlas, template matching, or, edge surface detection, have been applied to solve this task. However, applicability these methods usually limited by the characteristics images used in study datasets, while MRI varies with respect different protocols used, addition variability shapes. All variability, artifacts,...
In Brief Objectives The use of breast magnetic resonance imaging (MRI) as screening tool has been stalled by high examination costs. Scan protocols have lengthened to optimize specificity. Modern view-sharing sequences now enable ultrafast dynamic whole-breast MRI, allowing much shorter and more cost-effective procedures. This study evaluates whether information from MRI can be used replace standard preserve accuracy. Materials Methods We interleaved 20 time-resolved angiography with...
Currently, histopathological tissue examination by a pathologist represents the gold standard for breast lesion diagnostics. Automated classification of whole-slide images (WSIs) is challenging owing to wide range appearances benign lesions and visual similarity ductal carcinoma in-situ (DCIS) invasive at cellular level. Consequently, analysis high resolutions with large contextual area necessary. We present context-aware stacked convolutional neural networks (CNN) WSIs into normal/benign,...
Background In the first (prevalent) supplemental MRI screening round of Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial, a considerable number breast cancers were found at cost an increased false-positive rate (FPR). incident rounds, lower cancer detection (CDR) is expected due to smaller pool prevalent cancers, reduced FPR, availability prior examinations. Purpose To investigate performance indicators second (incidence round) DENSE trial. Materials Methods The trial...
Background Developments in artificial intelligence (AI) systems to assist radiologists reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system detect normal, moderate-risk, and suspicious a sample safely reduce radiologist workload evaluate across Breast Imaging Reporting Data System (BI-RADS) densities. Materials Methods This retrospective simulation study analyzed mammographic examination data consecutively collected from January...
Background Retrospective studies have suggested that using artificial intelligence (AI) may decrease the workload of radiologists while preserving mammography screening performance. Purpose To compare and performance for two cohorts women who underwent before after AI system implementation. Materials Methods This retrospective study included 50–69-year-old biennial in Capital Region Denmark. Before implementation (October 1, 2020, to November 17, 2021), all screenings involved double...
A method for automated determination of parenchymal patterns in mammograms has been developed that is insensitive to changes the mammographic imaging technique. The was designed study relation between breast cancer risk and density. It includes a new automatic segmentation pectoral muscle oblique mammograms, based on application Hough transform. technique classification distance transform subdivides tissue area into regions which skin line approximately equal. Features are calculated from...
Malignant densities in mammograms have an irregular appearance and frequently are surrounded by a radiating pattern of linear spicules. In this paper method is described to detect such stellate patterns. This based on statistical analysis map pixel orientations. If increase pixels pointing region found, marked as suspicious, especially if found many directions. Orientations the image intensity determined at each using multiscale approach. At given scale, accurate line-based orientation...
A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness mapping to a pixel determined by using physical model image acquisition. This based on the assumption that composed two types tissue, fat and parenchyma. Effective linear attenuation coefficients these tissues are derived empirical data as function tube voltage (kVp), anode material, filtration, compressed thickness. By employing these,...
To evaluate contrast enhancement patterns of urinary bladder cancer and surrounding structures to a fast dynamic first-pass magnetic resonance (MR) imaging technique in tumor node staging differentiation from postbiopsy effects.Sixty-one consecutive patients with histologically proved were referred undergo unenhanced MR 1-4 weeks after transurethral resection or biopsy. Subtraction time (to beginning enhancement) images acquired.Results T1- T2-weighted compared those obtained the plus...
Objectives To objectively evaluate automatic volumetric breast density assessment in Full-Field Digital Mammograms (FFDM) using measurements obtained from Magnetic Resonance Imaging (MRI). Material and Methods A commercially available method for estimation on FFDM is evaluated by comparing volume estimates 186 exams including mediolateral oblique (MLO) cranial-caudal (CC) views to objective reference standard MRI. Results Volumetric show high correlation with MRI data. Pearson's coefficients...
To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM). We collected a consecutive series of 111,898 DM examinations (2003–2011) from one unit the Dutch biennial program (age 50–75 years). Volumetric was assessed Volpara. determined measures for four categories comparable American College Radiology (ACR) breast categories. Of all examinations, 21.6% were categorized as category 1 ('almost...
Purpose To compare screen-film mammography with digital in a breast cancer screening program, focus on the clinical relevance of detected cancers. Materials and Methods The study was approved by regional medical ethics review board. Informed consent not required. Before nationwide transition to Dutch biennial performance studied three regions. For initial examinations, mediolateral oblique craniocaudal views were obtained each breast. In subsequent view standard. A added if indicated....
Breast density measurement is an important aspect in breast cancer diagnosis as dense tissue has been related to the risk of development. The purpose this study develop a method automatically compute MRI. framework combination image processing techniques segment and fibroglandular tissue. Intra- interpatient signal intensity variability initially corrected. segmented by detecting body-breast air-breast surfaces. Subsequently, area using expectation-maximization. A dataset 50 cases with...