Data Adaptive Few-shot Multi Label Segmentation with Foundation Model

• URL: http://arxiv.org/abs/2410.09759v1
• Date: Sun, 13 Oct 2024 07:29:13 GMT
• Title: Data Adaptive Few-shot Multi Label Segmentation with Foundation Model
• Authors: Gurunath Reddy, Dattesh Shanbhag, Deepa Anand,
• Abstract summary: State-of-the-art methods for few-shot segmentation suffer from sub-optimal performance for medical images. We propose foundation model (FM) based adapters for single label, multi-label localization and segmentation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The high cost of obtaining accurate annotations for image segmentation and localization makes the use of one and few shot algorithms attractive. Several state-of-the-art methods for few-shot segmentation have emerged, including text-based prompting for the task but suffer from sub-optimal performance for medical images. Leveraging sub-pixel level features of existing Vision Transformer (ViT) based foundation models for identifying similar region of interest (RoI) based on a single template image have been shown to be very effective for one shot segmentation and localization in medical images across modalities. However, such methods rely on assumption that template image and test image are well matched and simple correlation is sufficient to obtain correspondences. In practice, however such an approach can fail to generalize in clinical data due to patient pose changes, inter-protocol variations even within a single modality or extend to 3D data using single template image. Moreover, for multi-label tasks, the RoI identification has to be performed sequentially. In this work, we propose foundation model (FM) based adapters for single label, multi-label localization and segmentation to address these concerns. We demonstrate the efficacy of the proposed method for multiple segmentation and localization tasks for both 2D and 3D data as we well as clinical data with different poses and evaluate against the state of the art few shot segmentation methods.

Related papers

• Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models [17.461510586128874]
  We propose a novel method that adapts DINOv2 and Segment Anything Model 2 for retrieval-augmented few-shot medical image segmentation. Our approach uses DINOv2's feature as query to retrieve similar samples from limited annotated data, which are then encoded as memories and stored in memory bank.
  arXiv  Detail & Related papers  (2024-08-16T15:48:07Z)
• Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images [12.365801596593936]
  Medical image segmentation is one of the domains where sufficient annotated data is not available. We propose a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels. We show that the proposed simple but potent framework performs at par with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-12T15:38:51Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models [13.08275555017179]
  We propose ProMISe, a prompt-driven 3D medical image segmentation model using only a single point prompt. We evaluate our model on two public datasets for colon and pancreas tumor segmentations.
  arXiv  Detail & Related papers  (2023-10-30T16:49:03Z)
• A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers [0.0]
  We propose a simple framework to achieve fair image segmentation of multiple modalities using a single conditional model. We show that our framework outperforms other cross-modality segmentation methods on the Multi-Modality Whole Heart Conditional Challenge.
  arXiv  Detail & Related papers  (2023-10-09T09:51:44Z)
• 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)
• Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation [49.59567529191423]
  We propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D.
  arXiv  Detail & Related papers  (2022-03-07T19:02:26Z)
• Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization [29.470385509955687]
  We propose a novel dual-normalization module by leveraging the augmented source-similar and source-dissimilar images. Our method outperforms other state-of-the-art domain generalization methods.
  arXiv  Detail & Related papers  (2021-12-21T13:18:46Z)
• Automatic size and pose homogenization with spatial transformer network to improve and accelerate pediatric segmentation [51.916106055115755]
  We propose a new CNN architecture that is pose and scale invariant thanks to the use of Spatial Transformer Network (STN) Our architecture is composed of three sequential modules that are estimated together during training. We test the proposed method in kidney and renal tumor segmentation on abdominal pediatric CT scanners.
  arXiv  Detail & Related papers  (2021-07-06T14:50:03Z)
• A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection [93.38607559281601]
  We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions.
  arXiv  Detail & Related papers  (2021-04-29T17:49:48Z)
• SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes [56.387647750094466]
  Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples. Most of advanced solutions exploit a metric learning framework that performs segmentation through matching each pixel to a learned foreground prototype. This framework suffers from biased classification due to incomplete construction of sample pairs with the foreground prototype only.
  arXiv  Detail & Related papers  (2021-04-19T11:21:47Z)

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.