Biomedical and Health Sciences
This cell images dataset is collected using an ultrafast imaging system known as asymmetric-detection time-stretch optical microscopy (ATOM) for training and evaluation. This novel imaging approach can achieve label-free and high-contrast flow imaging with good cellular resolution images at a very high speed. Each acquired image belongs to one of the four classes: THP1, MCF7, MB231 and PBMC.
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Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies. Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.
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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/
The DataPort Repository contains the data used primarily for generating Figure 1.
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69 whole slide images for early gastric cancer diagnosis, evaluating the proposed variational energy network (VENet).
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The self-collected 30000 pathological images for gland segmentation, including training images and annotations.
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Pituitary adenoma (PA) is one of the most common tumors of the central nervous system, accounting for about 10%-25% of all cases. Although it is generally considered to be a benign tumor, the tumor can compress important tissue structures around it and cause corresponding symptoms. And surgical treatment is the preferred treatment for pituitary adenoma. Therefore, preoperative observation on MRI is important to provide anatomical information to plan the resection and predict the prognosis.
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This data was collected during a validation study of our Torso-Dynamics Estimation System (TES). The TES consisted of a Force Sensing Seat (FSS) and an inertial measurement unit (IMU) that measured the kinetics and kinematics of the subject's torso motions. The FSS estimated the 3D forces, 3D moments, and 2D COPs while the IMU estimated the 3D torso angles. To validate the TES, the FSS and IMU estimates were compared to gold standard research equipment (AMTI force plate and Qualisys motion capture system, respectively).
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This dataset contains the extracted parameter data for the deep patellar tendon reflexes of four test subjects. Each subject was tapped with a reflex hammer with soft, medium, and hard taps three times. The dataset was collected by interpreting the spectrogram images from processed radar data and motion capture data.
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The dataset analyzed in this study is the result of a systematic literature review and a crowdsourced mini-project that aimed to identify and validate metrics relevant to maternal and neonatal healthcare examinations. The study involved a diverse group of participants, including 193 registered medical personnel from reputable institutions and 161 non-medical individuals who were active on various social media platforms related to maternal and neonatal healthcare.
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The dataset contains motion capture data of the human hand of 20 healthy subjects acquired using two different motion capture technology (wearable IMU and camera-based). This database provides an opportunity to expand the fields of research involving the hands or their range of mobility. Indeed, using this database to train AI's net to recognise gestures/tasks is an excellent beginning point for expanding the field of human-robot collaboration.
This work is licensed under the Creative Commons Attribution 4.0 International License.
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