TBI Image/Text (TBI-IT): Comprehensive Text and Image Datasets for Traumatic Brain Injury Research
- URL: http://arxiv.org/abs/2403.09062v1
- Date: Thu, 14 Mar 2024 03:07:49 GMT
- Title: TBI Image/Text (TBI-IT): Comprehensive Text and Image Datasets for Traumatic Brain Injury Research
- Authors: Jie Li, Jiaying Wen, Tongxin Yang, Fenglin Cai, Miao Wei, Zhiwei Zhang, Li Jiang,
- Abstract summary: TBI-IT is a new dataset in the medical field of Traumatic Brain Injury (TBI)
It includes both electronic medical records (EMRs) and head CT images.
This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of TBI.
- Score: 11.098732957191748
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we introduce a new dataset in the medical field of Traumatic Brain Injury (TBI), called TBI-IT, which includes both electronic medical records (EMRs) and head CT images. This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of TBI. This dataset, built upon the foundation of standard text and image data, incorporates specific annotations within the EMRs, extracting key content from the text information, and categorizes the annotation content of imaging data into five types: brain midline, hematoma, left cerebral ventricle, right cerebral ventricle and fracture. TBI-IT aims to be a foundational dataset for feature learning in image segmentation tasks and named entity recognition.
Related papers
- Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI.
Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels.
The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
arXiv Detail & Related papers (2024-07-31T04:32:43Z) - HICH Image/Text (HICH-IT): Comprehensive Text and Image Datasets for
Hypertensive Intracerebral Hemorrhage Research [12.479936404475803]
We introduce a new dataset in the medical field of hypertensive intracerebral hemorrhage (HICH) called HICH-IT.
This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of HICH.
arXiv Detail & Related papers (2024-01-29T07:44:09Z) - Machine Learning Applications in Traumatic Brain Injury: A Spotlight on
Mild TBI [0.972285423076459]
We review the state-of-the-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI.
This review may serve as a source of inspiration for future research studies aimed at improving the diagnosis of TBI using data-driven approaches and standard diagnostic data.
arXiv Detail & Related papers (2024-01-08T01:29:00Z) - Brain-ID: Learning Contrast-agnostic Anatomical Representations for
Brain Imaging [11.06907516321673]
We introduce Brain-ID, an anatomical representation learning model for brain imaging.
With the proposed "mild-to-severe" intrasubject generation, Brain-ID is robust to the subject-specific brain anatomy.
We present new metrics to validate the intra- and inter-subject robustness, and evaluate their performance on four downstream applications.
arXiv Detail & Related papers (2023-11-28T16:16:10Z) - fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for
Multi-Subject Brain Activity Decoding [54.17776744076334]
We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training.
Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns.
Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
arXiv Detail & Related papers (2023-11-01T07:24:22Z) - The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting [50.01582455004711]
For brain tumor patients, the image acquisition time series typically starts with an already pathological scan.
Many algorithms are designed to analyze healthy brains and provide no guarantee for images featuring lesions.
Examples include, but are not limited to, algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction.
Here, the participants explore inpainting techniques to synthesize healthy brain scans from lesioned ones.
arXiv Detail & Related papers (2023-05-15T20:17:03Z) - Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline [54.93591298333767]
Brain diffuser is a diffusion based end-to-end brain network generative model.
It exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects.
For the case of Alzheimer's disease, the proposed model performs better than the results from existing toolkits on the Alzheimer's Disease Neuroimaging Initiative database.
arXiv Detail & Related papers (2023-03-11T14:04:58Z) - TBI-GAN: An Adversarial Learning Approach for Data Synthesis on
Traumatic Brain Segmentation [14.183809518138242]
We propose a novel medical image inpainting model named TBI-GAN to synthesize TBI MR scans with paired brain label maps.
The main strength of our TBI-GAN method is that it can generate TBI images and corresponding label maps simultaneously.
Experimental results show that the proposed TBI-GAN method can produce sufficient synthesized TBI images with high quality and valid label maps.
arXiv Detail & Related papers (2022-08-12T03:33:08Z) - UniToBrain dataset: a Brain Perfusion Dataset [2.02258267891574]
We present UniToBrain, the very first open-source dataset for "perfusion maps"
We propose a novel neural networks-based algorithm, using the European library ECVL and EDDL for the image processing and developing deep learning models respectively.
arXiv Detail & Related papers (2022-08-01T07:16:02Z) - 2021 BEETL Competition: Advancing Transfer Learning for Subject
Independence & Heterogenous EEG Data Sets [89.84774119537087]
We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI)
Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects.
Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
arXiv Detail & Related papers (2022-02-14T12:12:20Z) - Interpretation of 3D CNNs for Brain MRI Data Classification [56.895060189929055]
We extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans.
We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods.
arXiv Detail & Related papers (2020-06-20T17:56:46Z)
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.