Medical Imaging
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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The dataset folder is divided into two parts. The first part is the Train dataset, which contains 900 Kvasir-SEG data sets and 550 CVC-ClinicDB data sets, with a total of 1450 training images. image is the original image and masks are labels. The next is the test dataset, which contains the remaining images of Kvasir-SEG and CVC-ClinicDB as the test set, and all images of CVC-ColonDB, ETIS, and CVC-300 as the test set images.
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Osteoarthritis (OA) is a prevalent degenerative joint disease,particularly affecting the knees. Early and accurate detection of OA and its severity, often graded using the Kellgren-Lawrence (KL) scale, is crucial for timely intervention and management. This study explores the application of deep learning techniques to automatically detect OA and assign KL grades from knee X-ray images. We propose a novel deep learning architecture that effectively extracts relevant features from X-ray images and classifies them into different KL grades.
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168 Views
This dataset includes two 2D medical image segmentation benchmark.
1. OD/OC Segmentation in Fundus Image
This dataset conprises five sub-datasets: Drishti-GS, RIM-ONE-r3, ORIGA, REFUGE, and the validation set of REFUGE2. Each image is cropped around the optic disc area. The size of all images is 512×512. The manual pixel-wise annotation is stored as a PNG image with the same size as the corresponding fundus image with the following labels:
128: Optic Disc (Grey color)
0: Optic Cup (Black color)
255: Background (White color)
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Liver cancer treatment, especially for metastatic cases, poses significant challenges in accurately targeting tumours while sparing healthy tissue. Radioembolisation with yttrium-90 (Y-90) microspheres is a promising technique, but precise imaging of microsphere distribution is crucial. This study utilises T-PEPT, a novel Positron Emission Particle Tracking (PEPT) algorithm that combines topological data analysis with machine learning to identify Y-90 microsphere clusters in a digital twin of a patient's liver.
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86 Views
This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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The ultrasound video data were collected from two sets of neck ultrasound videos of ten healthy subjects at the Ultrasound Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine. Each subject included video files of two groups of LSCM, LSSCap, RSCM, and RSSCap. The video format is avi.
The MRI training data were sourced from three hospitals: Longhua Hospital, Shanghai University of Traditional Chinese Medicine; Huadong Hospital, Fudan University; and Shenzhen Traditional Chinese Medicine Hospital.
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551 Views
This collection of medical image datasets is a valuable resource for anyone involved in medical imaging and disease research. It includes a variety of images from different medical fields, all designed to support research in diagnosis and treatment. The datasets cover chest CT-scans, lung radiography, brain MRI, retinal imaging, and gastrointestinal tract imaging. The chest CT-scan dataset includes 867 images of normal lungs and three types of lung cancer—adenocarcinoma, large cell carcinoma, and squamous cell carcinoma—providing essential data for understanding lung cancer.
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This data abstract pertains to an ultrasound imaging dataset that includes imaging data from both CIRS phantoms and human carotid artery cross-sections. The dataset encompasses:
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75 Views