A5070127396- Cutaneous Melanoma Detection and Management
- AI in cancer detection
- Nonmelanoma Skin Cancer Studies
- Medical Imaging Techniques and Applications
- Skin Protection and Aging
- Cell Image Analysis Techniques
- Radiomics and Machine Learning in Medical Imaging
- Optical Coherence Tomography Applications
- Chromium effects and bioremediation
- Advanced X-ray and CT Imaging
- Iron Metabolism and Disorders
- Cutaneous lymphoproliferative disorders research
- Single-cell and spatial transcriptomics
- Advanced MRI Techniques and Applications
- Neuroendocrine Tumor Research Advances
- Trace Elements in Health
- Gene Regulatory Network Analysis
- Infectious Diseases and Mycology
- Lung Cancer Diagnosis and Treatment
- Radiation Dose and Imaging
- Digital Transformation in Industry
- Biomedical and Engineering Education
- Digital Imaging in Medicine
- Advanced Radiotherapy Techniques
- Neural dynamics and brain function
Kitware (United States)
2014-2024
Advanced Dermatology
2022
Memorial Sloan Kettering Cancer Center
2022
Microsoft (United States)
2022
Medical University of Vienna
2022
National and Kapodistrian University of Athens
2022
Melanoma Research Alliance
2022
Emory University
2022
University of Wisconsin–Madison
2016
University of Iowa
2016
This article describes the design, implementation, and results of latest installment dermoscopic image analysis benchmark challenge. The goal is to support research development algorithms for automated diagnosis melanoma, most lethal skin cancer. challenge was divided into 3 tasks: lesion segmentation, feature detection, disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), approximately 50 attendees,...
This work summarizes the results of largest skin image analysis challenge in world, hosted by International Skin Imaging Collaboration (ISIC), a global partnership that has organized world's public repository dermoscopic images skin. The was 2018 at Medical Image Computing and Computer Assisted Intervention (MICCAI) conference Granada, Spain. dataset included over 12,500 across 3 tasks. 900 users registered for data download, 115 submitted to lesion segmentation task, 25 attribute detection...
Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable accurately recognizing disease. As expertise in limited supply, automated systems identifying disease could save lives, reduce unnecessary biopsies, and costs. Toward this goal, we propose a system that combines recent developments deep learning established machine approaches, creating ensembles methods segmenting lesions, as well analyzing detected area surrounding...
Abstract Prior skin image datasets have not addressed patient-level information obtained from multiple lesions the same patient. Though artificial intelligence classification algorithms achieved expert-level performance in controlled studies examining single images, practice dermatologists base their judgment holistically on The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice,...
This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images skin lesions captured from 2010 to 2016 in facilities Hospital Cl\'inic Barcelona. With this we aim study problem unconstrained classification cancer, including found hard-to-diagnose locations (nails and mucosa), large which do not fit aperture dermoscopy device, hypo-pigmented lesions. The will be provided participants ISIC Challenge 2019, where they asked train algorithms classify cancer automatically.
In this article, we describe the design and implementation of a publicly accessible dermatology image analysis benchmark challenge. The goal challenge is to sup- port research development algorithms for automated diagnosis melanoma, lethal form skin cancer, from dermoscopic images. was divided into sub-challenges each task involved in analysis, including lesion segmentation, feature detection within lesion, classification melanoma. Training data included 900 A separate test dataset 379...
The use of artificial intelligence (AI) is accelerating in all aspects medicine and has the potential to transform clinical care dermatology workflows. However, develop image-based algorithms for applications, comprehensive criteria establishing development performance evaluation standards are required ensure product fairness, reliability, safety.
Abstract Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Clínic Barcelona, Spain. The BCN20000 dataset aims address problem unconstrained classification skin cancer, including lesions hard-to-diagnose locations such as those found nails mucosa, large which do not fit aperture...
Significance The digital twin paradigm holds great promise for medicine, even though many technical and scientific challenges remain to be overcome, most importantly the efficient integration of heterogeneous component models. This is an unsolved problem in industry. It has long been understood that such models need built a modular fashion, connecting together individual biological processes. In conventional implementations, however, dependency structure modules reflects dependencies among...
This article describes the design, implementation, and results of latest installment dermoscopic image analysis benchmark challenge. The goal is to support research development algorithms for automated diagnosis melanoma, most lethal skin cancer. challenge was divided into 3 tasks: lesion segmentation, feature detection, disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), approximately 50 attendees,...
ABSTRACT We report the generation of a multimodal cell census and atlas mammalian primary motor cortex (MOp or M1) as initial product BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved morphological electrophysiological properties, cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance...
To address the error introduced by computed tomography (CT) scanners when assessing volume and unidimensional measurement of solid tumors, we scanned a precision manufactured pocket phantom simultaneously with patients enrolled in lung cancer clinical trial. Dedicated software quantified bias random [Formula: see text], text] dimensions Teflon sphere also response evaluation criteria tumors measurements using both constant adaptive thresholding. We found that underestimation was essentially...
Iron is essential to the virulence of Aspergillus species, and restricting iron availability a critical mechanism antimicrobial host defense. Macrophages recruited site infection are at crux this process, employing multiple intersecting mechanisms orchestrate sequestration from pathogens. To gain an integrated understanding how achieved in aspergillosis, we generated transcriptomic time series response human monocyte-derived macrophages used available literature construct mechanistic...
Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable accurately recognizing disease. As expertise in limited supply, automated systems identifying disease could save lives, reduce unnecessary biopsies, and costs. Toward this goal, we propose a system that combines recent developments deep learning established machine approaches, creating ensembles methods segmenting lesions, as well analyzing detected area surrounding...
This paper presents a robust segmentation method based on multi-scale classification to identify the lesion boundary in dermoscopic images. Our proposed leverages collection of classifiers which are trained at various resolutions categorize each pixel as "lesion" or "surrounding skin". In detection phase, applied new The classifier outputs fused level build probability maps represent saliency maps. next step, Otsu thresholding is convert binary masks, determine border lesions. We compared...
A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00879-x.
A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00865-3.
A challenge in multicenter trials that use quantitative positron emission tomography (PET) imaging is the often unknown variability PET image values, typically measured as standardized uptake introduced by intersite differences global and resolution-dependent biases. We present a method for simultaneous monitoring of scanner calibration reconstructed resolution on per-scan basis using PET/computed (CT) "pocket" phantom. simulation phantom studies to optimize design construction PET/CT pocket...