Medical diffusion on a budget: Textual Inversion for medical image generation

• URL: http://arxiv.org/abs/2303.13430v2
• Date: Wed, 11 Sep 2024 14:40:19 GMT
• Title: Medical diffusion on a budget: Textual Inversion for medical image generation
• Authors: Bram de Wilde, Anindo Saha, Maarten de Rooij, Henkjan Huisman, Geert Litjens,
• Abstract summary: Training from scratch requires large captioned datasets and significant computational resources. This work shows that adapting pre-trained Stable Diffusion models to medical imaging modalities is achievable by training text embeddings. The trained embeddings are compact (less than 1 MB), enabling easy data sharing with reduced privacy concerns.
• Score: 3.0826983115939823
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models for text-to-image generation, known for their efficiency, accessibility, and quality, have gained popularity. While inference with these systems on consumer-grade GPUs is increasingly feasible, training from scratch requires large captioned datasets and significant computational resources. In medical image generation, the limited availability of large, publicly accessible datasets with text reports poses challenges due to legal and ethical concerns. This work shows that adapting pre-trained Stable Diffusion models to medical imaging modalities is achievable by training text embeddings using Textual Inversion. In this study, we experimented with small medical datasets (100 samples each from three modalities) and trained within hours to generate diagnostically accurate images, as judged by an expert radiologist. Experiments with Textual Inversion training and inference parameters reveal the necessity of larger embeddings and more examples in the medical domain. Classification experiments show an increase in diagnostic accuracy (AUC) for detecting prostate cancer on MRI, from 0.78 to 0.80. Further experiments demonstrate embedding flexibility through disease interpolation, combining pathologies, and inpainting for precise disease appearance control. The trained embeddings are compact (less than 1 MB), enabling easy data sharing with reduced privacy concerns.

Related papers

• Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis [22.0080610434872]
  We propose a controlled generation framework for synthetic images with annotations. We show that our approach can produce annotated lung CT images that can faithfully represent anatomy. Our experiments demonstrate that controlled generative frameworks of this nature can surpass nearly every state-of-the-art image generative model.
  arXiv  Detail & Related papers  (2024-09-07T01:19:02Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Multimodal Foundation Models Exploit Text to Make Medical Image Predictions [3.4230952713864373]
  We evaluate the mechanisms by which multimodal foundation models integrate and prioritize different data modalities, including images and text. Our results suggest that multimodal AI models may be useful in medical diagnostic reasoning but that their accuracy is largely driven, for better and worse, by their exploitation of text.
  arXiv  Detail & Related papers  (2023-11-09T18:48:02Z)
• EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model [4.057796755073023]
  We develop controllable diffusion models for medical image synthesis, called EMIT-Diff. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints.
  arXiv  Detail & Related papers  (2023-10-19T16:18:02Z)
• 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)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [60.437091462613544]
  We introduce XrayGPT, a novel conversational medical vision-language model. It can analyze and answer open-ended questions about chest radiographs. We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis [10.133715767542386]
  We propose a knowledge-driven and data-driven framework for lung disease diagnosis. We formulate diagnosis rules according to authoritative clinical medicine guidelines and learn the weights of rules from text data. A multimodal fusion consisting of text and image data is designed to infer the marginal probability of lung disease.
  arXiv  Detail & Related papers  (2022-02-09T04:12:30Z)
• Self-supervised Learning from 100 Million Medical Images [13.958840691105992]
  We propose a method for self-supervised learning of rich image features based on contrastive learning and online feature clustering. We leverage large training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging and ultrasonography. We highlight a number of advantages of this strategy on challenging image assessment problems in radiography, CT and MR.
  arXiv  Detail & Related papers  (2022-01-04T18:27:04Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)

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.