Improving Melanoma Pathology Accuracy through Computer Vision Techniques - the IMPACT Study
通过计算机视觉技术提高黑色素瘤病理学的准确性 - IMPACT 研究
基本信息
- 批准号:9174605
- 负责人:
- 金额:$ 40.81万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-08-01 至 2021-07-31
- 项目状态:已结题
- 来源:
- 关键词:AdultAlgorithmsArchitectureAreaAssociation LearningBehaviorBenignBiological Neural NetworksBiopsyCaringCell NucleusCessation of lifeCharacteristicsClinicalCommunitiesComputational TechniqueComputer Vision SystemsComputer-Assisted Image AnalysisComputersConsensusDataDatabasesDecision MakingDermatopathologyDetectionDevelopmentDiagnosisDiagnosticDiagnostic ErrorsDiagnostic ImagingElderlyEvaluationEventFundingGlassGoalsGraphHumanImageImage AnalysisIncidenceIndividualInternationalLeadLesionMachine LearningMalignant NeoplasmsMethodsMicroscopicMitoticPathologistPathologyPatientsPatternPerformancePhysiciansPrecancerous melanosisProcessPropertyReference StandardsResearchScanningSkinSlideSpecimenStagingSystemTechniquesTechnologyTissuesTrainingUnited States National Institutes of HealthWorkbasecancer diagnosisdiagnostic accuracydigital imagingimprovedinnovationinterestmaltreatmentmelanocytemelanomanovelskin lesionstemtoolvisual tracking
项目摘要
ABSTRACT
This proposal will help to improve the accuracy of diagnosing melanoma and melanocytic lesions. The incidence of melanoma is rising faster than any other cancer, and ~1 in 50 U.S. adults will be diagnosed with melanoma this year alone. Our research team has noted substantial diagnostic errors in interpreting skin biopsies of melanocytic lesions; pathologists disagree in up to 60% of cases of invasive melanoma, which can lead to substantial patient harm. Our proposal uses computer technology to analyze whole-slide digital images of glass slides in order to improve the diagnosis of melanocytic lesions. Using data from an ongoing NIH study, we will digitize and study a set of 240 skin biopsy cases that includes a full spectrum of benign to invasive melanoma diagnoses. Each biopsy case has a reference consensus diagnosis developed by a panel of three international experts in dermatopathology and new data will be available from 160 practicing U.S. community pathologists, providing a uniquely rich clinical database that is the largest of its kind. This project will include novel computational techniques, including the detection of both cellular-level and architectural entities, and the use of a combination of feature-based and deep neural network classifiers. Our specific aims are: 1. To detect cellular-level entities in digitized whole slide images of melanocytic skin lesions. 2. To detect structural (architectural) entities in digitized whole slide images of melanocytic skin lesions. 3. To develop an automated diagnosis system that can classify digitized slide images into one of five possible diagnostic classes: benign; atypical lesions; melanoma in situ; invasive melanoma stage T1a; and invasive melanoma stage ≥T1b. In our proposed study, we are innovatively using computer image analysis algorithms and machine learning. This technology has the potential to improve the diagnostic accuracy of pathologists by providing an analytical, undeviating review to assist humans in this difficult task.
摘要
这一建议将有助于提高诊断黑色素瘤和黑色素细胞病变的准确性。黑色素瘤的发病率上升速度比任何其他癌症都快,仅今年一年,每50名美国成年人中就有1人被诊断出患有黑色素瘤。我们的研究团队注意到在解释黑色素细胞病变的皮肤活检时存在大量的诊断错误;病理学家对高达60%的侵袭性黑色素瘤病例不同意,这可能导致患者严重伤害。我们的建议使用计算机技术来分析玻璃载玻片的全载玻片数字图像,以提高黑素细胞病变的诊断。利用NIH正在进行的一项研究的数据,我们将对一组240例皮肤活检病例进行分析和研究,其中包括良性到侵袭性黑色素瘤诊断的全谱。每个活检病例都有一个由三名国际皮肤病理学专家组成的小组开发的参考共识诊断,160名执业美国社区病理学家将提供新的数据,提供一个独特丰富的临床数据库,这是同类数据库中最大的。该项目将包括新的计算技术,包括细胞级和建筑实体的检测,以及基于特征和深度神经网络分类器的组合使用。我们的具体目标是:1.在黑色素细胞皮肤病变的数字化全切片图像中检测细胞水平实体。2.检测黑色素细胞皮肤病变数字化全切片图像中的结构(建筑)实体。3.开发一种自动诊断系统,可将数字化载玻片图像分类为五种可能的诊断类别之一:良性;非典型病变;原位黑色素瘤;侵袭性黑色素瘤T1 a期;侵袭性黑色素瘤≥ T1 b期。在我们提出的研究中,我们创新地使用计算机图像分析算法和机器学习。这项技术有可能通过提供分析性的、不偏离的回顾来帮助人类完成这项艰巨的任务,从而提高病理学家的诊断准确性。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
JOANN G ELMORE其他文献
JOANN G ELMORE的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('JOANN G ELMORE', 18)}}的其他基金
Metacognition and the Diagnostic Process in Pathology
元认知和病理学诊断过程
- 批准号:
10284893 - 财政年份:2021
- 资助金额:
$ 40.81万 - 项目类别:
Reader Accuracy in Pathology Interpretation and Diagnosis: Perception and Cognition (RAPID-PC)
病理解释和诊断的读者准确性:感知和认知 (RAPID-PC)
- 批准号:
10388503 - 财政年份:2018
- 资助金额:
$ 40.81万 - 项目类别:
Reader Accuracy in Pathology Interpretation and Diagnosis: Perception and Cognition (RAPID-PC)
病理解释和诊断的读者准确性:感知和认知 (RAPID-PC)
- 批准号:
10165663 - 财政年份:2018
- 资助金额:
$ 40.81万 - 项目类别:
Reader Accuracy in Pathology Interpretation and Diagnosis: Perception and Cognition (RAPID-PC)
病理解释和诊断的读者准确性:感知和认知 (RAPID-PC)
- 批准号:
9925189 - 财政年份:2018
- 资助金额:
$ 40.81万 - 项目类别:
Reader Accuracy in Pathology Interpretation and Diagnosis: Perception and Cognition (RAPID-PC)
病理解释和诊断的读者准确性:感知和认知 (RAPID-PC)
- 批准号:
10407524 - 财政年份:2018
- 资助金额:
$ 40.81万 - 项目类别:
Improving Melanoma Pathology Accuracy through Computer Vision Techniques - the IMPACT Study
通过计算机视觉技术提高黑色素瘤病理学的准确性 - IMPACT 研究
- 批准号:
9976466 - 财政年份:2017
- 资助金额:
$ 40.81万 - 项目类别:
Improving Melanoma Pathology Accuracy through Computer Vision Techniques - the IMPACT Study
通过计算机视觉技术提高黑色素瘤病理学的准确性 - IMPACT 研究
- 批准号:
9751222 - 财政年份:2017
- 资助金额:
$ 40.81万 - 项目类别:
Reducing Errors in the Diagnosis of Melanoma and Melanocytic Lesions
减少黑色素瘤和黑色素细胞病变的诊断错误
- 批准号:
9005424 - 财政年份:2016
- 资助金额:
$ 40.81万 - 项目类别:
Digital Pathology_Accuracy Viewing Behavior and Image Characterization
数字病理学_观看行为和图像表征的准确性
- 批准号:
8771432 - 财政年份:2012
- 资助金额:
$ 40.81万 - 项目类别:
Digital Pathology_Accuracy Viewing Behavior and Image Characterization
数字病理学_观看行为和图像表征的准确性
- 批准号:
8970690 - 财政年份:2012
- 资助金额:
$ 40.81万 - 项目类别:
相似海外基金
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 40.81万 - 项目类别:
Continuing Grant
Collaborative Research: SHF: Small: Artificial Intelligence of Things (AIoT): Theory, Architecture, and Algorithms
合作研究:SHF:小型:物联网人工智能 (AIoT):理论、架构和算法
- 批准号:
2221742 - 财政年份:2022
- 资助金额:
$ 40.81万 - 项目类别:
Standard Grant
Collaborative Research: SHF: Small: Artificial Intelligence of Things (AIoT): Theory, Architecture, and Algorithms
合作研究:SHF:小型:物联网人工智能 (AIoT):理论、架构和算法
- 批准号:
2221741 - 财政年份:2022
- 资助金额:
$ 40.81万 - 项目类别:
Standard Grant
Algorithms and Architecture for Super Terabit Flexible Multicarrier Coherent Optical Transmission
超太比特灵活多载波相干光传输的算法和架构
- 批准号:
533529-2018 - 财政年份:2020
- 资助金额:
$ 40.81万 - 项目类别:
Collaborative Research and Development Grants
OAC Core: Small: Architecture and Network-aware Partitioning Algorithms for Scalable PDE Solvers
OAC 核心:小型:可扩展 PDE 求解器的架构和网络感知分区算法
- 批准号:
2008772 - 财政年份:2020
- 资助金额:
$ 40.81万 - 项目类别:
Standard Grant
Algorithms and Architecture for Super Terabit Flexible Multicarrier Coherent Optical Transmission
超太比特灵活多载波相干光传输的算法和架构
- 批准号:
533529-2018 - 财政年份:2019
- 资助金额:
$ 40.81万 - 项目类别:
Collaborative Research and Development Grants
Visualization of FPGA CAD Algorithms and Target Architecture
FPGA CAD 算法和目标架构的可视化
- 批准号:
541812-2019 - 财政年份:2019
- 资助金额:
$ 40.81万 - 项目类别:
University Undergraduate Student Research Awards
Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging
合作研究:ABI 创新:从 3D 成像恢复根结构的算法
- 批准号:
1759836 - 财政年份:2018
- 资助金额:
$ 40.81万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging
合作研究:ABI 创新:从 3D 成像恢复根结构的算法
- 批准号:
1759796 - 财政年份:2018
- 资助金额:
$ 40.81万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging
合作研究:ABI 创新:从 3D 成像恢复根结构的算法
- 批准号:
1759807 - 财政年份:2018
- 资助金额:
$ 40.81万 - 项目类别:
Standard Grant