## Overview
This dataset, featured in the RSNA Intracranial Hemorrhage Detection challenge on Kaggle, offers a rich collection of brain CT images. It is meticulously categorized into seven distinct classes: 'none', 'epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid', and 'subdural'. Each category is represented by 1000 DICOM files, providing a balanced and extensive dataset for analysis and machine learning applications.
## Dataset Details
__Categories__: None, Epidural, Intraparenchymal, Intraventricular, Subarachnoid, Subdural
__Files__: 1000 DICOM files per category
__Total Images__: 6000 DICOM files
__Source__: RSNA Intracranial Hemorrhage Detection Challenge on Kaggle (https://www.kaggle.com/competitions/rsna-intracranial-hemorrhage-detection/data)
##Use Case##
This dataset is ideal for researchers and practitioners working on medical imaging. It provides a substantial resource for developing and testing algorithms in the detection of various types of intracranial hemorrhages.
Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is currently one of the powerful tools for the clinical diagnosis of dementia such as Alzheimer's Disease (AD). Meanwhile, MR imaging, being non-radioactive and having high contrast resolution, is highly accessible in clinical settings. Therefore, this dataset intends to use FDG-PET images as the Ground Truth for evaluating AD, for the development of predicting AD patients using MR images.
This dataset includes an AD group and a control group (Healthy Group). The determination of the image diagnosis group is made by neurology specialists based on comprehensive judgment using clinically relevant information.
Each set of data contains one set of MRI T1 images and one set of FDG-PET images. The image format is DICOM, and all images have been anonymized.
To obtain the clinical information and related documentation, please contact the administrator.
Datasets can be set as public, allowing the general public to directly access the data. If set as a private dataset, only members of the same organization can access it, and it cannot be searched for. Dataset administrators should first create a public dataset, which includes documentation on private dataset-related information and contact details. Then, dataset administrators should add a custom field named "needapply" with a value of "true" to the dataset. This dataset will provide an application process; upon approval, dataset administrators can add user accounts to the list of collaborators, enabling them to access private dataset-related information.
The dataset maintainer is responsible for approving dataset usage requests on this platform. Please log in to the
[Dataset Application Portal](https://da.dmc.nycu.edu.tw) using your maintainer account to approve.
To add collaborators, simply search for the email account before the "@" symbol. For example, to add the user "docliu@nycu.edu.tw," enter "docliu" to add them as a user.
The naming conventions for datasets are as follows:
Public: ihd-ct
Private: ihd-ct-private
##Overview##
This dataset is at the core of a dementia research project focused on the exploration and diagnosis of dementia using advanced imaging technologies. It integrates data collected through Single-Photon Emission Computed Tomography (SPECT).
##Dataset Composition##
The dataset comprises images and data obtained from SPECT scans. SPECT imaging helps in assessing cerebral blood flow.
##Dataset Scope and Objectives##
The primary objective of this dataset is to advance the understanding of dementia through the lens of neuroimaging. By examining the relationship between local cerebral blood flow and metabolic function, the study aims to identify patterns that correlate with dementia. The ultimate goal is to enhance the diagnosis and understanding of dementia by correlating cerebral blood flow patterns with glucose metabolism changes in the brain.
##Citation Guideline##
Pending
This dataset consists of 2D chest CT images with annotated left atrium locations, along with clinical information to predict the appropriate treatment mode for atrial fibrillation surgery. The goal is to enhance the success rate and safety of atrial fibrillation procedures by providing detailed imaging and clinical data to support optimal treatment planning.
