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AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation
v1v2v3 (latest)

AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation

IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2023
28 August 2023
Chengyin Li
Prashant Khanduri
Yao Qiang
Rafi Ibn Sultan
I. Chetty
D. Zhu
    MedIm
ArXiv (abs)PDFHTML

Papers citing "AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation"

4 / 4 papers shown
Title
Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting
Supervise Less, See More: Training-free Nuclear Instance Segmentation with Prototype-Guided Prompting
Wen Zhang
Qin Ren
Wenjing Liu
Haibin Ling
Chenyu You
MedImVLM
339
0
0
25 Nov 2025
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
Han Gu
Haoyu Dong
Jichen Yang
Maciej A. Mazurowski
MedImVLM
323
40
0
15 Apr 2024
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image
  Segmentation
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
Cheng Chen
Juzheng Miao
Dufan Wu
Zhiling Yan
Sekeun Kim
...
Lichao Sun
Xiang Li
Tianming Liu
Pheng-Ann Heng
Shijie Zhao
MedIm
245
97
0
16 Sep 2023
Interpretability-Aware Vision Transformer
Interpretability-Aware Vision Transformer
Yao Qiang
Chengyin Li
Prashant Khanduri
D. Zhu
ViT
492
12
0
14 Sep 2023
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