Medical Imaging
Traditional Thai medicine (TTM) is an increasingly popular treatment option. Tongue diagnosis is a highly efficient method for determining overall health, practiced by TTM practitioners. However, the diagnosis naturally varies depending on the practitioner's expertise. In this work, we propose tongue image analysis using raw pixels and artificial intelligence (AI) to support TTM diagnoses. The target classification of Tri-Dhat consists of three classes: Vata, Pitta, and Kapha. We utilize our own organized genuine datasets collected from our university's TTM hospital.
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We conducted a retrospective collection, covering 167 children who were examined and treated at the Children's Hospital of Chongqing Medical University from March 12, 2014 to January 7, 2022, with a total of 1634 IRI image sequences. This study has been registered with the Chinese Clinical Trial Registry, registration number ChiCTR2200058971, and complied with the provisions of the Declaration of Helsinki (DoH). The study was approved by the Institutional Ethical Review Board (document number 2022,69), and a waiver of informed consent was obtained.
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The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in particular diastolic volume and ejection fraction).
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313 Views
This dataset contains RF (Radio Frequency) signals obtained from simulations, which model ultrasound propagation in cortical bone.
The simulations were designed to provide insights into the behaviour of ultrasound waves in cortical bone tissues, both in intact and pathological conditions. The dataset covers a wide range of parameters, including varying thickness (1-8 mm), porosity (1-20%), and frequency (1-8 MHz), allowing to explore the impact of these factors on ultrasound signal characteristics.
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409 Views
With the goal of improving machine learning approaches in inverse scattering, we provide an experimental data set collected with a 2D near-field microwave imaging system. Machine learning approaches often train solely on synthetic data, and one of the reasons for this is that no experimentally-derived public data set exists. The imaging system consists of 24 antennas surrounding the imaging region, connected via a switch to a vector network analyzer. The data set contains over 1000 full Scattering parameter scans of five targets at numerous positions from 3-5 GHz.
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780 Views
This is a dataset on normal and early stage (stage I and II) endometrial cancer, comprising a total of 300 MRI images of patients (100 normal, 100 stage I and II), 207 patients (77 healthy, 100 stage IA (50 stage IA, 50 stage IB), and 30 stage II patients. From January 1, 2018 to December 31, 2020, he underwent 1.5-T MRI in Fujian Maternal and Child Health Hospital, with an average age of 55.7 years. Patient age The images in this dataset were all provided by the Radiology Department of Fujian Provincial Maternal and Child Health Care Hospital and may contain privacy concerns.
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460 Views
This is a part of the COVID-CTset dataset for testing and training the network. This dataset contains 12058 CT slices. It was gathered from Negin medical center that is located at Sari in Iran. This medical center uses a SOMATOM Scope model and syngo CT VC30-easyIQ software version for capturing and visualizing the lung HRCT radiology images from the patients. The format of the exported radiology images was 16-bit grayscale DICOM format with 512*512 pixels resolution.
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141 Views
The study included 50 epilepsy patients undergoing long-term video-EEG monitoring at the Epilepsy Center of Guangdong 999 Brain Hospital. The inclusion criteria for patients were as follows: (1) VEEG reports confirming definite epileptic seizures, (2) complete video data containing both seizure and non-seizure periods, (3) no intentional interference during patient seizures, and no occlusion of the patient, such as patients were covered by quilts.
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244 Views
3D datasets used in Toward-ground-truth optical coherence tomography. Guangming Ni et al., "Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data", 2023 There are two dataset: OCT-R1 and OCT-R2. OCT-R1 contains three-dimensional (3D) data collected from 41 human eyes using a BM-400K BMizar (Topi Ltd.) OCT scanner at Sichuan Provincial People's Hospital. To enhance the diversity of the data, we performed scans over two different ranges.
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290 Views
CT RECIST response, as measured by the change of tumor diameter, can accurately reflect objective response rate for advanced NSCLC patients. However, there exists obvious discordant between CT RECIST response and prognostic indicators. Thus, our study aimed to identify a new CT RECIST response indicator at the early treatment stage to reflect the prognosis more accurately.We studied 916 tumor lesions obtained through deep learning and found that the shape of the lesions was irregular.
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126 Views