Search alternatives:
segmentation algorithm » selection algorithm (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
feature segmentation » feature representation (Expand Search), feature selection (Expand Search), image segmentation (Expand Search)
image feature » image features (Expand Search), scale feature (Expand Search), imaging features (Expand Search)
task derived » risks derived (Expand Search), ipsc derived (Expand Search), data derived (Expand Search)
binary task » binary mask (Expand Search)
Showing 81 - 98 results of 98 for search '(( binary task derived optimization algorithm ) OR ( binary image feature segmentation algorithm ))', query time: 0.60s Refine Results
  1. 81
    Latent state estimations for experimental data with interruptions for subject 12.

    Latent state estimations for experimental data with interruptions for subject 12. by Samiul Alam (18563323)

    Published 2024
    Save to List
    Saved in:
  2. 82
    Latent state estimations for experimental data with interruptions for subject 20.

    Latent state estimations for experimental data with interruptions for subject 20. by Samiul Alam (18563323)

    Published 2024
    Save to List
    Saved in:
  3. 83
    An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%.

    An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. by Zhengdong Wang (35299)

    Published 2025
    Save to List
    Saved in:
  4. 84
    DataSheet_1_Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.docx

    DataSheet_1_Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.docx by Shima Sepehri (11574997)

    Published 2021
    Save to List
    Saved in:
  5. 85
    Fig 1 -

    Fig 1 - by Monjoy Saha (12239792)

    Published 2022
    Save to List
    Saved in:
  6. 86
    Dense block structure.

    Dense block structure. by Xiaoqin Wu (470428)

    Published 2024
    “…This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. …”
    Save to List
    Saved in:
  7. 87
    Structure diagram of a transition layer.

    Structure diagram of a transition layer. by Xiaoqin Wu (470428)

    Published 2024
    “…This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. …”
    Save to List
    Saved in:
  8. 88
    Dense-U-net network structure.

    Dense-U-net network structure. by Xiaoqin Wu (470428)

    Published 2024
    “…This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. …”
    Save to List
    Saved in:
  9. 89
    Experimental setup and trajectory estimation methodology.

    Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

    Published 2023
    Save to List
    Saved in:
  10. 90
    Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  11. 91
    Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  12. 92
    Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  13. 93
    Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  14. 94
    Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg

    Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:
  15. 95
    Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx

    Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx by Bettina Katalin Budai (13951317)

    Published 2022
    “…After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. …”
    Save to List
    Saved in:
  16. 96
    Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX

    Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX by Bettina Katalin Budai (13951317)

    Published 2022
    “…After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. …”
    Save to List
    Saved in:
  17. 97
    Data_Sheet_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.XLSX

    Data_Sheet_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.XLSX by Bettina Katalin Budai (13951317)

    Published 2022
    “…After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. …”
    Save to List
    Saved in:
  18. 98
    Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx

    Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx by Varun Sendilraj (19732510)

    Published 2024
    “…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”
    Save to List
    Saved in:

Search Tools: