Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
- URL: http://arxiv.org/abs/2406.00956v1
- Date: Mon, 3 Jun 2024 03:16:25 GMT
- Title: Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
- Authors: Tianyu Huang, Tao Zhou, Weidi Xie, Shuo Wang, Qi Dou, Yizhe Zhang,
- Abstract summary: 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.
- Score: 52.172885882728174
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical images. In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. These rectifications typically entail manual or semi-manual corrections employing state-of-the-art annotation tools. Motivated by this process, 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. To improve the effectiveness and efficiency of online learning when integrated with large-scale vision models like SAM, we propose a new method called Auxiliary Online Learning (AuxOL). AuxOL creates and applies a small auxiliary model (specialist) in conjunction with SAM (generalist), entails adaptive online-batch and adaptive segmentation fusion. Experiments conducted on eight datasets covering four medical imaging modalities validate the effectiveness of the proposed method. Our work proposes and validates a new, practical, and effective approach for enhancing SA on downstream segmentation tasks (e.g., medical image segmentation).
Related papers
- MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation [2.2585213273821716]
We propose a novel framework, called MedCLIP-SAM, that combines CLIP and SAM models to generate segmentation of clinical scans.
By extensively testing three diverse segmentation tasks and medical image modalities, our proposed framework has demonstrated excellent accuracy.
arXiv Detail & Related papers (2024-03-29T15:59:11Z) - Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation [20.557472889654758]
The Segment Anything Model (SAM) gained significant success in natural image segmentation.
Unlike natural images, many tissues and lesions in medical images have blurry boundaries and may be ambiguous.
We propose a novel module called the Uncertainty-aware Adapter, which efficiently fine-tune SAM for uncertainty-aware medical image segmentation.
arXiv Detail & Related papers (2024-03-16T14:11:54Z) - 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) - Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation [7.747608350830482]
We present a novel framework for weakly supervised lesion segmentation in early breast ultrasound images.
Our method uses morphological enhancement and class activation map (CAM)-guided localization.
This approach does not require pixel-level annotation, thereby reducing the cost of data annotation.
arXiv Detail & Related papers (2023-11-18T22:06:04Z) - 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) - 3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable
Medical Image Segmentation [56.50064853710202]
We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation.
Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
arXiv Detail & Related papers (2023-06-23T12:09:52Z) - 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) - Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights.
Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion.
Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
arXiv Detail & Related papers (2023-04-10T17:58:22Z) - PCA: Semi-supervised Segmentation with Patch Confidence Adversarial
Training [52.895952593202054]
We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation.
PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state.
Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
arXiv Detail & Related papers (2022-07-24T07:45:47Z)
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.