Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2412.13742v1
• Date: Wed, 18 Dec 2024 11:19:23 GMT
• Title: Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
• Authors: Kaiwen Huang, Tao Zhou, Huazhu Fu, Yizhe Zhang, Yi Zhou, Chen Gong, Dong Liang,
• Abstract summary: We propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Our model outperforms the state-of-the-art semi-supervised segmentation approaches.
• Score: 47.789013598970925
• License:
• Abstract: The limited availability of labeled data has driven advancements in semi-supervised learning for medical image segmentation. Modern large-scale models tailored for general segmentation, such as the Segment Anything Model (SAM), have revealed robust generalization capabilities. However, applying these models directly to medical image segmentation still exposes performance degradation. In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation. Firstly, we propose a Multi-view Co-training (MC) strategy that employs two distinct sub-networks to employ a co-teaching paradigm, resulting in more robust outcomes. Secondly, we present a Learnable Prompt Strategy (LPS) to dynamically produce dense prompts and integrate an adapter to fine-tune SAM specifically for medical image segmentation tasks. Moreover, we propose SAM-induced Knowledge Distillation (SKD) to transfer useful knowledge from SAM to two sub-networks, enabling them to learn from SAM's predictions and alleviate the effects of incorrect pseudo-labels during training. Notably, the predictions generated by our subnets are used to produce mask prompts for SAM, facilitating effective inter-module information exchange. Extensive experimental results on various medical segmentation tasks demonstrate that our model outperforms the state-of-the-art semi-supervised segmentation approaches. Crucially, our SAM distillation framework can be seamlessly integrated into other semi-supervised segmentation methods to enhance performance. The code will be released upon acceptance of this manuscript at: https://github.com/taozh2017/KnowSAM

Related papers

• SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation [13.037264314135033]
  We propose the SEmantic-Guided SAM (SEG-SAM), a unified medical segmentation model that incorporates semantic medical knowledge. First, to avoid the potential conflict between binary and semantic predictions, we introduce a semantic-aware decoder independent of SAM's original decoder. We solicit key characteristics of medical categories from large language models and incorporate them into SEG-SAM through a text-to-vision semantic module. In the end, we introduce the cross-mask spatial alignment strategy to encourage greater overlap between the predicted masks from SEG-SAM's two decoders.
  arXiv  Detail & Related papers  (2024-12-17T08:29:13Z)
• Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation [52.172885882728174]
  In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. We introduce a novel approach that leverages the advantages of online machine learning to enhance Segment Anything (SA) during test time. We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images.
  arXiv  Detail & Related papers  (2024-06-03T03:16:25Z)
• Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding [15.401507589312702]
  This paper introduces H-SAM, a prompt-free adaptation of the Segment Anything Model (SAM) for efficient fine-tuning of medical images. In the initial stage, H-SAM employs SAM's original decoder to generate a prior probabilistic mask, guiding a more intricate decoding process. Our H-SAM demonstrates a 4.78% improvement in average Dice compared to existing prompt-free SAM variants.
  arXiv  Detail & Related papers  (2024-03-27T05:55:16Z)
• Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively [69.97238935096094]
  The Open-Vocabulary SAM is a SAM-inspired model designed for simultaneous interactive segmentation and recognition. Our method can segment and recognize approximately 22,000 classes.
  arXiv  Detail & Related papers  (2024-01-05T18:59:22Z)
• SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization [23.28335241083164]
  Semi-supervised methods can improve the performance by utilizing unlabeled data. SemiSAM significantly improves the performance of existing semi-supervised frameworks when only one or a few labeled images are available.
  arXiv  Detail & Related papers  (2023-12-11T12:03:30Z)
• Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation [45.15517909664628]
  Polyp segmentation plays a vital role in accurately locating polyps at an early stage. pixel-wise annotation for polyp images by physicians during the diagnosis is both time-consuming and expensive. We propose a novel SAM-guided Collaborative Learning Network (SAM-CLNet) for scribble-supervised polyp segmentation.
  arXiv  Detail & Related papers  (2023-12-01T03:07:13Z)
• Guided Prompting in SAM for Weakly Supervised Cell Segmentation in Histopathological Images [27.14641973632063]
  This paper focuses on using weak supervision -- annotation from related tasks -- to induce a segmenter. Recent foundation models, such as Segment Anything (SAM), can use prompts to leverage additional supervision during inference. All SAM-based solutions hugely outperform existing weakly supervised image segmentation models, obtaining 9-15 pt Dice gains.
  arXiv  Detail & Related papers  (2023-11-29T11:18:48Z)
• SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation [65.52097667738884]
  We introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to integrate surgical-specific information with SAM's pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning.
  arXiv  Detail & Related papers  (2023-08-17T02:51:01Z)
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [51.770805270588625]
  The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Recent studies and individual experiments have shown that SAM underperforms in medical image segmentation. We propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model.
  arXiv  Detail & Related papers  (2023-04-25T07:34:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.