Artificial Intelligence (AI) Assisted Detection of FGFR3 Alterations in Bladder Cancer From Scanned Whole Slide Images (WSI) of H&E Sections

被引:0
|
作者
Kunz, Jeremy [1 ]
Wang, Yikan [1 ]
Bernhard, Jan [1 ]
Ramirezpadron, Ruben [1 ]
Al-Ahmadie, Hikmat [2 ]
Vanderbilt, Chad [2 ]
Kanan, Christopher [1 ]
Oakley, Joe [1 ]
Klimstra, David [1 ]
Fuchs, Thomas [1 ]
机构
[1] Paige AI, New York, NY USA
[2] Mem Sloan Kettering Canc Ctr, 1275 York Ave, New York, NY 10021 USA
关键词
D O I
暂无
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
783
引用
收藏
页码:S985 / S985
页数:1
相关论文
共 29 条
  • [21] Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study
    Wu, Shaoxu
    Hong, Guibin
    Xu, Abai
    Zeng, Hong
    Chen, Xulin
    Wang, Yun
    Luo, Yun
    Wu, Peng
    Liu, Cundong
    Jiang, Ning
    Dang, Qiang
    Yang, Cheng
    Liu, Bohao
    Shen, Runnan
    Chen, Zeshi
    Liao, Chengxiao
    Lin, Zhen
    Wang, Jin
    Lin, Tianxin
    LANCET ONCOLOGY, 2023, 24 (04): : 360 - 370
  • [22] Artificial Intelligence-Powered Tumor Purity Assessment From H&E Whole Slide Images Correlates with Consensus Purity Estimation Based on Pathological Examination and Next-generation Sequencing
    Park, Gahee
    Choi, Sangjoon
    Kim, Seokhwi
    Cho, Soo Ick
    Jung, Wonkyung
    Ryu, Jeongun
    Ma, Minuk
    Yoo, Donggeun
    Paeng, Kyunghyun
    Ock, Chan-Young
    Song, Sanghoon
    Song, Heon
    Pereira, Sergio
    Park, Seonwook
    MODERN PATHOLOGY, 2022, 35 (SUPPL 2) : 1256 - 1257
  • [23] ARTIFICIAL INTELLIGENCE (AI) BASED DEEP LEARNING MODELS CAN ACCURATELY CLASSIFY HIGH-GRADE DYSPLASIA (HGD) FROM WHOLE SLIDE IMAGES (WSI) OF ADENOMATOUS COLON POLYPS
    Das, Amit
    Prasad, Anil R.
    GASTROENTEROLOGY, 2023, 164 (06) : S1165 - S1165
  • [24] Artificial Intelligence-Powered Tumor Purity Assessment From H&E Whole Slide Images Correlates with Consensus Purity Estimation Based on Pathological Examination and Next-generation Sequencing
    Park, Gahee
    Choi, Sangjoon
    Kim, Seokhwi
    Cho, Soo Ick
    Jung, Wonkyung
    Ryu, Jeongun
    Ma, Minuk
    Yoo, Donggeun
    Paeng, Kyunghyun
    Ock, Chan-Young
    Song, Sanghoon
    Song, Heon
    Pereira, Sergio
    Park, Seonwook
    LABORATORY INVESTIGATION, 2022, 102 (SUPPL 1) : 1256 - 1257
  • [25] Deep Learning Identifies Microsatellite Instability in H&E Whole Slide Images from Prostate, Esophageal, and Gastric Cancers and Generalizes across Cancer Types
    Joshi, Rohan
    Kruger, Andrew
    Moore, Elle
    Jones, Ryan
    Stumpe, Martin
    MODERN PATHOLOGY, 2022, 35 (SUPPL 2) : 1078 - 1079
  • [26] Deep Learning Identifies Microsatellite Instability in H&E Whole Slide Images from Prostate, Esophageal, and Gastric Cancers and Generalizes across Cancer Types
    Joshi, Rohan
    Kruger, Andrew
    Moore, Elle
    Jones, Ryan
    Stumpe, Martin
    LABORATORY INVESTIGATION, 2022, 102 (SUPPL 1) : 1078 - 1079
  • [27] DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer
    Schirris, Yoni
    Gavves, Efstratios
    Nederlof, Iris
    Horlings, Hugo Mark
    Teuwen, Jonas
    MEDICAL IMAGE ANALYSIS, 2022, 79
  • [28] Detection of Ki67 Hot-Spots of Invasive Breast Cancer Based on Convolutional Neural Networks Applied to Mutual Information of H&E and Ki67 Whole Slide Images
    Swiderska-Chadaj, Zaneta
    Gallego, Jaime
    Gonzalez-Lopez, Lucia
    Bueno, Gloria
    APPLIED SCIENCES-BASEL, 2020, 10 (21): : 1 - 18
  • [29] Development of an AI-based algorithm to quantify eosinophils in H&E images from colorectal cancer (CRC) tissue sections guided by biomarker staining using multiplex immunofluorescence imaging
    Hanifi, Arezoo
    Blain, Elizabeth
    Hargrove, James
    Lock, Jeff
    Tran, Nam
    Chizhevsky, Vladislav
    Au, Qingyan
    CANCER RESEARCH, 2024, 84 (06)