Generative AI for Medical Imaging: extending the MONAI Framework
- URL: http://arxiv.org/abs/2307.15208v1
- Date: Thu, 27 Jul 2023 21:58:26 GMT
- Title: Generative AI for Medical Imaging: extending the MONAI Framework
- Authors: Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel
Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb,
Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong
Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna
Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M.
Jorge Cardoso
- Abstract summary: We present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models.
Our platform reproduces state-of-art studies in a standardised way involving different architectures.
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios.
- Score: 29.152928147704507
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in generative AI have brought incredible breakthroughs in
several areas, including medical imaging. These generative models have
tremendous potential not only to help safely share medical data via synthetic
datasets but also to perform an array of diverse applications, such as anomaly
detection, image-to-image translation, denoising, and MRI reconstruction.
However, due to the complexity of these models, their implementation and
reproducibility can be difficult. This complexity can hinder progress, act as a
use barrier, and dissuade the comparison of new methods with existing works. In
this study, we present MONAI Generative Models, a freely available open-source
platform that allows researchers and developers to easily train, evaluate, and
deploy generative models and related applications. Our platform reproduces
state-of-art studies in a standardised way involving different architectures
(such as diffusion models, autoregressive transformers, and GANs), and provides
pre-trained models for the community. We have implemented these models in a
generalisable fashion, illustrating that their results can be extended to 2D or
3D scenarios, including medical images with different modalities (like CT, MRI,
and X-Ray data) and from different anatomical areas. Finally, we adopt a
modular and extensible approach, ensuring long-term maintainability and the
extension of current applications for future features.
Related papers
- Computation-Efficient Era: A Comprehensive Survey of State Space Models in Medical Image Analysis [8.115549269867403]
State Space Models (SSMs) have garnered immense interest lately in sequential modeling and visual representation learning.
Capitalizing on the advances in computer vision, medical imaging has heralded a new epoch with Mamba models.
arXiv Detail & Related papers (2024-06-05T16:29:03Z) - OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models
in Medicine [55.29668193415034]
We present OpenMEDLab, an open-source platform for multi-modality foundation models.
It encapsulates solutions of pioneering attempts in prompting and fine-tuning large language and vision models for frontline clinical and bioinformatic applications.
It opens access to a group of pre-trained foundation models for various medical image modalities, clinical text, protein engineering, etc.
arXiv Detail & Related papers (2024-02-28T03:51:02Z) - A 3D generative model of pathological multi-modal MR images and
segmentations [3.4806591877889375]
We propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations.
The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations.
We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data.
arXiv Detail & Related papers (2023-11-08T09:36:37Z) - StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized
Image-Dialogue Data [129.92449761766025]
We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning.
This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models.
Our research includes comprehensive experiments conducted on various datasets.
arXiv Detail & Related papers (2023-08-20T12:43:52Z) - On Sensitivity and Robustness of Normalization Schemes to Input
Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input.
DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases.
We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
arXiv Detail & Related papers (2023-06-23T03:09:03Z) - medigan: A Python Library of Pretrained Generative Models for Enriched
Data Access in Medical Imaging [3.8568465270960264]
medigan is a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library.
It allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code.
The library's scalability and design is demonstrated by its growing number of integrated and readily-usable pretrained generative models.
arXiv Detail & Related papers (2022-09-28T23:45:33Z) - Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions.
Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity.
A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
arXiv Detail & Related papers (2022-04-09T13:04:36Z) - A Learnable Variational Model for Joint Multimodal MRI Reconstruction
and Synthesis [4.056490719080639]
We propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI.
The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality.
arXiv Detail & Related papers (2022-04-08T01:35:19Z) - DIME: Fine-grained Interpretations of Multimodal Models via Disentangled
Local Explanations [119.1953397679783]
We focus on advancing the state-of-the-art in interpreting multimodal models.
Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
arXiv Detail & Related papers (2022-03-03T20:52:47Z) - CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in
Medical Imaging AI [1.359138408203412]
We build and test a medical imaging AI drift monitoring workflow that tracks data and model drift without contemporaneous ground truth.
Key contributions include (1) proof-of-concept for medical imaging drift detection including use of VAE and domain specific statistical methods.
This work has important implications for addressing the translation gap related to continuous medical imaging AI model monitoring in dynamic healthcare environments.
arXiv Detail & Related papers (2022-02-06T18:58:35Z) - Modality Completion via Gaussian Process Prior Variational Autoencoders
for Multi-Modal Glioma Segmentation [75.58395328700821]
We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan.
MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations.
We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
arXiv Detail & Related papers (2021-07-07T19:06:34Z)
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.