Medical Imaging
This dataset contains high-resolution retinal fundus images collected from 495 unique subjects from Eye Care hospital in Aizawl, Mizoram, for diabetic retinopathy (DR) detection and classification. The images were captured over five years using the OCT RS 330 device, which features a 45° field of view (33° for small-pupil imaging), a focal length of 45.7 mm, and a 6.25 mm sensor width. Each image was acquired at a resolution of 3000x3000 pixels, ensuring high diagnostic quality and the visibility of subtle features like microaneurysms, exudates, and hemorrhages.
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About the data
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This Dataset is a self-harm dataset developed by ZIOVISION Co. Ltd. It consists of 1,120 videos. Actors were hired to simulate self-harm behaviors, and the scenes were recorded using four cameras to ensure full coverage without blind spots. Self-harm behaviors in the dataset are limited to "cutting" actions targeting specific body parts. The designated self-harm areas include the wrists, forearms, and thighs.
The full dataset can be accesssed through https://github.com/zv-ai/ZV_Self-harm-Dataset.git
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Brain tumors are among the most severe and life-threatening conditions affecting both children and adults. They constitute approximately 85-90% of all primary Central Nervous System (CNS) tumors, with an estimated 11,700 new cases diagnosed annually. The 5-year survival rate for individuals with malignant brain or CNS tumors is alarmingly low, at 34% for men and 36% for women. Brain tumors are categorized into various types, including benign, malignant, and pituitary tumors.
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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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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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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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