Full-Scale Indexing and Semantic Annotation of CT Imaging: Boosting FAIRness

• URL: http://arxiv.org/abs/2406.15340v1
• Date: Fri, 21 Jun 2024 17:55:22 GMT
• Title: Full-Scale Indexing and Semantic Annotation of CT Imaging: Boosting FAIRness
• Authors: Hannes Ulrich, Robin Hendel, Santiago Pazmino, Björn Bergh, Björn Schreiweis,
• Abstract summary: The proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability. The metadata is standardized with HL7 FHIR resources to enable efficient data recognition and data exchange between research projects. The study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 230,000 CT image series and over 8 million SNOMED CT annotations.
• Score: 0.41942958779358674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: The integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning. However, the reliability of AI models is highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets. Results: The proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability. Through an automated indexing process, CT image series are semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations. The metadata is standardized with HL7 FHIR resources to enable efficient data recognition and data exchange between research projects. Conclusions: The study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 230,000 CT image series and over 8 million SNOMED CT annotations. The standardized representation using HL7 FHIR resources improves discoverability and facilitates interoperability, providing a foundation for the FAIRness of medical imaging data. However, developing automated annotation methods that can keep pace with growing clinical datasets remains a challenge to ensure continued progress in large-scale integration and indexing of medical imaging for advanced healthcare AI applications.

Related papers

• MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation [0.0]
  We introduce MAPUNetR, a novel architecture that synergizes the strengths of transformer models with the proven U-Net framework for medical image segmentation. Our model addresses the resolution preservation challenge and incorporates attention maps highlighting segmented regions, increasing accuracy and interpretability. Our experiments show that the model maintains stable performance and potential as a powerful tool for medical image segmentation in clinical practice.
  arXiv  Detail & Related papers  (2024-10-29T16:52:57Z)
• Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation [3.7274206780843477]
  We introduce a robust and versatile framework that combines AI and crowdsourcing to improve the quality and quantity of medical image datasets. Our approach utilise a user-friendly online platform that enables a diverse group of crowd annotators to label medical images efficiently. We employ pix2pixGAN, a generative AI model, to expand the training dataset with synthetic images that capture realistic morphological features.
  arXiv  Detail & Related papers  (2024-09-04T21:22:54Z)
• MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions [0.13108652488669734]
  integration of neural-network-based systems into clinical practice is limited by challenges related to domain generalization and robustness. We create and open-source MedMNIST-C, a benchmark dataset based on the MedMNIST+ collection covering 12 datasets and 9 imaging modalities.
  arXiv  Detail & Related papers  (2024-06-25T13:20:39Z)
• HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling [4.44283662576491]
  We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods.
  arXiv  Detail & Related papers  (2024-03-20T05:50:04Z)
• 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)
• 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)
• HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using CT Images and Text [0.0]
  Prosthetic Joint Infection (PJI) is a prevalent and severe complication. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques.
  arXiv  Detail & Related papers  (2023-05-29T11:25:57Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.