Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation

• URL: http://arxiv.org/abs/2408.03651v2
• Date: Wed, 4 Sep 2024 08:23:00 GMT
• Title: Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation
• Authors: Mingya Zhang, Liang Wang, Zhihao Chen, Yiyuan Ge, Xianping Tao,
• Abstract summary: We propose Path-SAM2, which for the first time adapts the SAM2 model to cater to the task of pathological semantic segmentation. We integrate the largest pretrained vision encoder for histopathology (UNI) with the original SAM2 encoder, adding more pathology-based prior knowledge. In three adenoma pathological datasets, Path-SAM2 has achieved state-of-the-art performance.
• Score: 6.721564277355789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The semantic segmentation task in pathology plays an indispensable role in assisting physicians in determining the condition of tissue lesions. With the proposal of Segment Anything Model (SAM), more and more foundation models have seen rapid development in the field of image segmentation. Recently, SAM2 has garnered widespread attention in both natural image and medical image segmentation. Compared to SAM, it has significantly improved in terms of segmentation accuracy and generalization performance. We compared the foundational models based on SAM and found that their performance in semantic segmentation of pathological images was hardly satisfactory. In this paper, we propose Path-SAM2, which for the first time adapts the SAM2 model to cater to the task of pathological semantic segmentation. We integrate the largest pretrained vision encoder for histopathology (UNI) with the original SAM2 encoder, adding more pathology-based prior knowledge. Additionally, we introduce a learnable Kolmogorov-Arnold Networks (KAN) classification module to replace the manual prompt process. In three adenoma pathological datasets, Path-SAM2 has achieved state-of-the-art performance.This study demonstrates the great potential of adapting SAM2 to pathology image segmentation tasks. We plan to release the code and model weights for this paper at: https://github.com/simzhangbest/SAM2PATH

Related papers

• Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation [47.789013598970925]
  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.
  arXiv  Detail & Related papers  (2024-12-18T11:19:23Z)
• 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)
• DB-SAM: Delving into High Quality Universal Medical Image Segmentation [100.63434169944853]
  We propose a dual-branch adapted SAM framework, named DB-SAM, to bridge the gap between natural and 2D/3D medical data. Our proposed DB-SAM achieves an absolute gain of 8.8%, compared to a recent medical SAM adapter in the literature.
  arXiv  Detail & Related papers  (2024-10-05T14:36:43Z)
• SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation [51.90445260276897]
  We prove that the Segment Anything Model 2 (SAM2) can be a strong encoder for U-shaped segmentation models. We propose a simple but effective framework, termed SAM2-UNet, for versatile image segmentation.
  arXiv  Detail & Related papers  (2024-08-16T17:55:38Z)
• SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More [16.40994541980171]
  This paper introduces SAM2-Adapter, the first adapter designed to overcome the persistent limitations observed in SAM2. It builds on the SAM-Adapter's strengths, offering enhanced generalizability and composability for diverse applications. We show the potential and encourage the research community to leverage the SAM2 model with our SAM2-Adapter.
  arXiv  Detail & Related papers  (2024-08-08T16:40:15Z)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• SAM-Med2D [34.82072231983896]
  We introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images. We first collect and curate approximately 4.6M images and 19.7M masks from public and private datasets. We fine-tune the encoder and decoder of the original SAM to obtain a well-performed SAM-Med2D.
  arXiv  Detail & Related papers  (2023-08-30T17:59:02Z)
• SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology [28.62539784951823]
  Foundation models, such as the Segment Anything Model (SAM), have been recently proposed for universal use in segmentation tasks. In this work, we adapt SAM for semantic segmentation by introducing trainable class prompts, followed by further enhancements through the incorporation of a pathology foundation model. Our framework, SAM-Path enhances SAM's ability to conduct semantic segmentation in digital pathology without human input prompts.
  arXiv  Detail & Related papers  (2023-07-12T20:15:25Z)
• 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)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)

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.