GMISeg: General Medical Image Segmentation without Re-Training

• URL: http://arxiv.org/abs/2311.12539v5
• Date: Mon, 16 Sep 2024 17:41:55 GMT
• Title: GMISeg: General Medical Image Segmentation without Re-Training
• Authors: Jing Xu,
• Abstract summary: Deep learning models often struggle to be generalisable to unknown tasks involving new anatomical structures, labels, or shapes. Here I developed a general model that can solve unknown medical image segmentation tasks without requiring additional training. I evaluated the performance of the proposed method on medical image datasets with different imaging modalities and anatomical structures.
• Score: 6.6467547151592505
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning models have become the dominant method for medical image segmentation. However, they often struggle to be generalisable to unknown tasks involving new anatomical structures, labels, or shapes. In these cases, the model needs to be re-trained for the new tasks, posing a significant challenge for non-machine learning experts and requiring a considerable time investment. Here I developed a general model that can solve unknown medical image segmentation tasks without requiring additional training. Given an example set of images and visual prompts for defining new segmentation tasks, GMISeg (General Medical Image Segmentation) leverages a pre-trained image encoder based on ViT and applies a low-rank fine-tuning strategy to the prompt encoder and mask decoder to fine-tune the model without in an efficient manner. I evaluated the performance of the proposed method on medical image datasets with different imaging modalities and anatomical structures. The proposed method facilitated the deployment of pre-trained AI models to new segmentation works in a user-friendly way.

Related papers

• Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation [49.5901368256326]
  We propose a novel Domain-Adaptive Prompt framework for fine-tuning the Segment Anything Model (termed as DAPSAM) in segmenting medical images. Our DAPSAM achieves state-of-the-art performance on two medical image segmentation tasks with different modalities.
  arXiv  Detail & Related papers  (2024-09-19T07:28:33Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• UniverSeg: Universal Medical Image Segmentation [16.19510845046103]
  We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. We have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks.
  arXiv  Detail & Related papers  (2023-04-12T19:36:46Z)
• Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation [0.16490701092527607]
  We propose an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated.
  arXiv  Detail & Related papers  (2022-07-17T13:28:52Z)
• Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation [62.49837463676111]
  We propose a novel scheme of episodic training with task augmentation on medical imaging classification. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting.
  arXiv  Detail & Related papers  (2021-06-13T03:56:59Z)
• Uncertainty guided semi-supervised segmentation of retinal layers in OCT images [4.046207281399144]
  We propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network. The proposed framework is a key contribution and applicable for biomedical image segmentation across various imaging modalities.
  arXiv  Detail & Related papers  (2021-03-02T23:14:25Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks [54.00217496410142]
  We propose an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation. Our approach allows to train image segmentation models without the need to acquire expensive annotations. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods.
  arXiv  Detail & Related papers  (2020-07-09T01:39:39Z)
• Semi-supervised few-shot learning for medical image segmentation [21.349705243254423]
  Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm. We propose a novel few-shot learning framework for semantic segmentation, where unlabeled images are also made available at each episode. We show that including unlabeled surrogate tasks in the episodic training leads to more powerful feature representations.
  arXiv  Detail & Related papers  (2020-03-18T20:37:18Z)

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.