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Models/AutoSAM

AutoSAM

Reported on 12 benchmarks across 1 task · 1 paper · 11 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical12 results

  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    S measure· 2023-06-10
    0.815
    best: 0.9 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    Sensitivity· 2023-06-10
    0.672
    best: 83.7 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    mean E-measure· 2023-06-10
    0.855
    best: 93.8 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    mean F-measure· 2023-06-10
    0.774
    best: 93.8 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    weighted F-measure· 2023-06-10
    0.716
    best: 0.794 (SALI)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    Dice· 2023-06-10
    0.759
    best: 0.902 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    S-Measure· 2023-06-10
    0.822
    best: 0.894 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    Sensitivity· 2023-06-10
    0.726
    best: 0.852 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    mean E-measure· 2023-06-10
    0.866
    best: 0.941 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    mean F-measure· 2023-06-10
    0.764
    best: 0.932 (YOLO-SAM 2)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Hard (Unseen)
    weighted F-measure· 2023-06-10
    0.714
    best: 0.79 (SALI)
    SOTA
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370
  • Medical Image SegmentationonSUN-SEG-Easy (Unseen)
    Dice· 2023-06-10
    0.753
    best: 0.9 (YOLO-SAM 2)
    AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt EncoderarXiv:2306.06370