Medical Imaging

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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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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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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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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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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The MalariaSD dataset encompasses diverse stages and classes of malaria parasites, including Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, and Plasmodium ovale, categorized into four phases: ring, schizont, trophozoite, and gametocyte.

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Prostate cancer is a major global health challenge. In this study, we present an approach for the early detection of prostate cancer through the semantic segmentation of adenocarcinoma tissues, specifically focusing on distinguishing Gleason patterns 3 and 4. Our method leverages deep learning techniques to improve diagnostic accuracy and enhance patient treatment strategies. We developed a new dataset comprising 100 digitized whole-slide images of prostate needle core biopsy specimens, publicly available for research purposes.

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This is an open source data set from Kaggle. The original link is 

<a href="https://www.kaggle.com/datasets/farjanakabirsamanta/osteoarthritis-prediction?resource=download">IEEE DataPort</a>

The dataset, provided by the University of Florida and the OAI organisation in September 2018, is 428MB in size and contains 5778 training samples, 1656 test samples and 826 validation samples. 

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UTERUS: The uterus dataset (Huang et al. 2021) collected from the treatment device HIFU Pro2008 of Shenzhen ProHuiren Company. The dataset comprises 495 HIFU treatment ultrasound monitoring images of uterus, with 330 images randomly selected for training, 50 images for validating and 115 images for testing. The target region for treatment is the tumor region in the ultrasound image, as marked by professional doctors, and the image size is 448×544 pixels.

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This synthetic dataset or phantom consists of 3 jpg format databases, in the two-dimensional (2-D) domain, which are identified as follows:

 

DB1: Ground Truth

 

DB2: Speckle noise with zero mean and 0.005 standard deviation

 

DB3: Speckle noise with zero mean and 0.05 standard deviation

 

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