Medical Imaging
We introduce two novel datasets for cell motility and wound healing research: the Wound Healing Assay Dataset (WHAD) and the Cell Adhesion and Motility Assay Dataset (CAMAD). WHAD comprises time-lapse phase-contrast images of wound healing assays using genetically modified MCF10A and MCF7 cells, while CAMAD includes MDA-MB-231 and RAW264.7 cells cultured on various substrates. These datasets offer diverse experimental conditions, comprehensive annotations, and high-quality imaging data, addressing gaps in existing resources.
- Categories:
740 Views
Ultrasound (US) provides non-invasive visualization of tissue morphology for musculoskeletal disorders. Spatial Frequency Analysis (SFA) of US images quantitatively characterizes tissue morphology, and has shown the ability to distinguish healthy from pathological tendons. However, the impact of US machine settings on SFA for tendon pathology remains underexplored. Methods: Five participants with unilateral supraspinatus tendon partial tears were imaged bilaterally to examine how variations in US settings (frequency, dynamic range, gain) influence SFA parameters.
- Categories:
159 Views
Machine learning (ML) in the medical domain faces challenges due to limited high-quality data. This study addresses the scarcity of echocardiography images (echoCG) by generating synthetic data using state-of-the-art generative models. We evaluated a cycle-consistent generative adversarial network (CycleGAN), contrastive unpaired translation (CUT) method, and latent diffusion model (Stable Diffusion 1.5).
- Categories:
150 Views
<p class="MsoNormal"><span lang="EN-US">This is a dataset about Helicobacter pylori (H.pylori). The dataset consists of 994 pathology images of H. pylori stained using immunohistochemistry. Each image is of size 1916x1010 pixels and is accompanied by an annotation file. The annotations are done using point annotations, where the annotation file records the coordinates of H. pylori in each image.</span><span><span lang="EN-US">The dataset provides a valuable resource for researchers and practitioners working in the field of H.
- Categories:
236 Views
Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a practice that could present challenges for patients in remote areas with inadequate transportation and healthcare infrastructure. This has led to the development of algorithms designed for the analysis and follow-up of wound images, which perform image-processing tasks such as classification, detection, and segmentation.
- Categories:
873 Views
This dataset named "Chest X-ray images for Multiple diseases" is a medium sized dataset we collected and produced in 2024 from various sources to predict various Chest-X-ray diseases using Deep learning techniques, primarily from Radiopaedia.org, coronacases.org, Kaggle contains 1000 images for each of the disease namely TB,pneumonia,Covid-19,Normal. This dataset is designed to support the evaluation and development of algorithms to predict various chest x-ray diseases.
- Categories:
936 Views
This dataset contains MRI scans from 5 MS and 5 NMO cases from the Universiti Teknologi MARA (UiTM) hospital Malaysia. The brain lesions in the MRI scans have been annotated by a consultant radiologist from Pakistan Institute of Medical Sciences (PIMS) Islamabad Pakistan. The ground truth lesion masks are available as png files, whereas the brain scans are available as jpg files.
- Categories:
339 Views
This dataset comprises a comprehensive collection of augmented MRI images of brain tumors, organized into two distinct folders: 'Yes' and 'No'. The 'Yes' folder contains 9,828 images of brain tumors, while the 'No' folder includes 9,546 images that do not exhibit brain tumors, resulting in a total of 19,374 images. All images are in PNG format, ensuring high-quality and consistent resolution suitable for various machine learning and medical imaging research applications.
- Categories:
772 Views
This dataset consists of 462 field of views of Giemsa(dye)-stained and field(dye)-stained thin blood smear images acquired using an iPhone 10 mobile phone with a 12MP camera. The phone was attached to an Olympus microscope with 1000× objective lens. Half of the acquired images are red blood cells with a normal morphology and the other half have a Rouleaux formation morphology.
- Categories:
843 Views
This dataset acompanies our article titled "Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation", Computers in Biology and Medicine, 2024. This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration.
- Categories:
244 Views