Enhancing Swin Transformer with Semantic Attention for Explainable Prediction: A Case Study with Lung Cancer CT Images

被引：0
|
作者：
Rangnekar, A. [1 ]
Jiang, J. [2 ]
Veeraraghavan, H. [1 ]
机构：
[1] Mem Sloan Kettering Canc Ctr, 1275 York Ave, New York, NY 10021 USA
[2] Mem Sloan Kettering Canc Ctr, Dept Med Phys, New York, NY 10021 USA
来源：
MEDICAL PHYSICS | 2024年 / 51卷 / 09期
关键词：
D O I：
暂无
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
引用
收藏
页码：6580 / 6580
页数：1
相关论文
共 50 条
  • [31] Prediction of VEGF and EGFR Expression in Peripheral Lung Cancer Based on the Radiomics Model of Spectral CT Enhanced Images
    Wu, Linhua
    Li, Jian
    Ruan, Xiaowei
    Ren, Jialiang
    Ping, Xuejun
    Chen, Bing
    INTERNATIONAL JOURNAL OF GENERAL MEDICINE, 2022, 15 : 6725 - 6738
  • [32] Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques
    Rajasekar, Vani
    Vaishnnave, M. P.
    Premkumar, S.
    Sarveshwaran, Velliangiri
    Rangaraaj, V.
    RESULTS IN ENGINEERING, 2023, 18
  • [33] Prediction of lung cancer risk in ground-glass nodules using deep learning from CT images
    Vachani, A.
    Zeb, S.
    Steltz, J.
    Hickes, W.
    Clarke, M.
    Kamara, A.
    Spechko, K.
    Padley, G.
    Bartlett, E.
    Freitag, L.
    Scarsbrook, A.
    Padley, S.
    Gleeson, F.
    ANNALS OF ONCOLOGY, 2023, 34 : S734 - S734
  • [34] The clinical test in a prospective study of a CAD system for lung cancer based on helical CT images
    Kubota, K
    Kubo, M
    Kawata, Y
    Niki, N
    Eguchi, K
    Omatsu, H
    Kakinuma, R
    Kaneko, M
    Moriyama, N
    MEDICAL IMAGING 2002: IMAGE PROCESSING, VOL 1-3, 2002, 4684 : 1270 - 1279
  • [35] MAU-Net: Multiple Attention 3D U-Net for Lung Cancer Segmentation on CT Images
    Chen, Wei
    Yang, Fengchang
    Zhang, Xianru
    Xu, Xin
    Qiao, Xu
    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021), 2021, 192 : 543 - 552
  • [36] Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning
    Tong, Yizhi
    Arimura, Hidetaka
    Yoshitake, Tadamasa
    Cui, Yunhao
    Kodama, Takumi
    Shioyama, Yoshiyuki
    Wirestam, Ronnie
    Yabuuchi, Hidetake
    APPLIED SCIENCES-BASEL, 2024, 14 (08):
  • [37] Cascaded-Recalibrated Multiple Instance Deep Model for Pathologic-Level Lung Cancer Prediction in CT Images
    Wang, Qingfeng
    Zhou, Ying
    Huang, Jun
    Liu, Zhiqin
    Zhang, Weidong
    Liu, Qiyu
    Cheng, Jie-Zhi
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [38] DECOUPLE-COUPLE NETWORK FOR DRUG-RESISTANT EGFR MUTATION SUBTYPE PREDICTION WITH LUNG CANCER CT IMAGES
    Zhu, Yongbei
    Wang, Liusu
    Yu, He
    Liu, Meili
    Zhang, Mingyu
    Li, Weimin
    Wang, Shuo
    Tian, Jie
    2022 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (IEEE ISBI 2022), 2022,
  • [39] Prediction of Lung Cancer from Low-Resolution Nodules in CT-Scan Images by using Deep Features
    Gupta, Anand
    Das, Sagorika
    Khurana, Tarasha
    Suri, Kamakshi
    2018 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2018, : 531 - 537
  • [40] Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images
    Le, Viet Huan
    Minh, Tran Nguyen Tuan
    Kha, Quang Hien
    Le, Nguyen Quoc Khanh
    JOURNAL OF MEDICAL SYSTEMS, 2025, 49 (01)
12345