Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
- URL: http://arxiv.org/abs/2406.05565v1
- Date: Sat, 8 Jun 2024 20:07:39 GMT
- Title: Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
- Authors: Sucheng Ren, Xiaoke Huang, Xianhang Li, Junfei Xiao, Jieru Mei, Zeyu Wang, Alan Yuille, Yuyin Zhou,
- Abstract summary: This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks.
MVG employs an in-context generation strategy that standardizes the handling of inputs and outputs as images.
Our results consistently establish MVG's superior performance, outperforming existing vision generalists, such as Painter and LVM.
- Score: 30.300087629262666
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks -- such as cross-modal synthesis, image segmentation, denoising, and inpainting -- within a unified image-to-image generation framework. Specifically, MVG employs an in-context generation strategy that standardizes the handling of inputs and outputs as images. By treating these tasks as an image generation process conditioned on prompt image-label pairs and input images, this approach enables a flexible unification of various tasks, even those spanning different modalities and datasets. To capitalize on both local and global context, we design a hybrid method combining masked image modeling with autoregressive training for conditional image generation. This hybrid approach yields the most robust performance across all involved medical imaging tasks. To rigorously evaluate MVG's capabilities, we curated the first comprehensive generalist medical vision benchmark, comprising 13 datasets and spanning four imaging modalities (CT, MRI, X-ray, and micro-ultrasound). Our results consistently establish MVG's superior performance, outperforming existing vision generalists, such as Painter and LVM. Furthermore, MVG exhibits strong scalability, with its performance demonstrably improving when trained on a more diverse set of tasks, and can be effectively adapted to unseen datasets with only minimal task-specific samples. The code is available at \url{https://github.com/OliverRensu/MVG}.
Related papers
- Generative Medical Segmentation [5.4613210257624605]
Generative Medical (GMS) is a novel approach leveraging a generative model to perform image segmentation.
GMS employs a robust pre-trained vision foundation model to extract latent representations for images and corresponding ground truth masks.
The design of GMS leads to fewer trainable parameters in the model which reduces the risk of overfitting and enhances its capability.
arXiv Detail & Related papers (2024-03-27T02:16:04Z) - VISION-MAE: A Foundation Model for Medical Image Segmentation and
Classification [36.8105960525233]
We present a novel foundation model, VISION-MAE, specifically designed for medical imaging.
VISION-MAE is trained on a dataset of 2.5 million unlabeled images from various modalities.
It is then adapted to classification and segmentation tasks using explicit labels.
arXiv Detail & Related papers (2024-02-01T21:45:12Z) - Gene-induced Multimodal Pre-training for Image-omic Classification [20.465959546613554]
This paper proposes a Gene-induced Multimodal Pre-training framework, which jointly incorporates genomics and Whole Slide Images (WSIs) for classification tasks.
Experimental results on the TCGA dataset show the superiority of our network architectures and our pre-training framework, achieving 99.47% in accuracy for image-omic classification.
arXiv Detail & Related papers (2023-09-06T04:30:15Z) - 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) - Customizing General-Purpose Foundation Models for Medical Report
Generation [64.31265734687182]
The scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks.
We propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs) in computer vision and natural language processing.
arXiv Detail & Related papers (2023-06-09T03:02:36Z) - Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation [38.61227663176952]
We propose a shift towards universal medical image segmentation, a paradigm aiming to build medical image understanding foundation models.
We develop Hermes, a novel context-prior learning approach to address the challenges of data heterogeneity and annotation differences in medical image segmentation.
arXiv Detail & Related papers (2023-06-04T17:39:08Z) - Multi-task Paired Masking with Alignment Modeling for Medical
Vision-Language Pre-training [55.56609500764344]
We propose a unified framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework.
We also introduce a Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual information to assist report reconstruction.
arXiv Detail & Related papers (2023-05-13T13:53:48Z) - AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context
Processing for Representation Learning of Giga-pixel Images [53.29794593104923]
We present a novel concept of shared-context processing for whole slide histopathology images.
AMIGO uses the celluar graph within the tissue to provide a single representation for a patient.
We show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data.
arXiv Detail & Related papers (2023-03-01T23:37:45Z) - MedSegDiff-V2: Diffusion based Medical Image Segmentation with
Transformer [53.575573940055335]
We propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2.
We verify its effectiveness on 20 medical image segmentation tasks with different image modalities.
arXiv Detail & Related papers (2023-01-19T03:42:36Z) - Learning to Exploit Temporal Structure for Biomedical Vision-Language
Processing [53.89917396428747]
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities.
We explicitly account for prior images and reports when available during both training and fine-tuning.
Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model.
arXiv Detail & Related papers (2023-01-11T16:35:33Z) - Multi-modal Understanding and Generation for Medical Images and Text via
Vision-Language Pre-Training [5.119201893752376]
We propose Medical Vision Language Learner (MedViLL) which adopts a Transformer-based architecture combined with a novel multimodal attention masking scheme.
We empirically demonstrate the superior downstream task performance of MedViLL against various baselines including task-specific architectures.
arXiv Detail & Related papers (2021-05-24T15:14:09Z)
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.