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- Data for: Non-sedated functional imaging based on deep synchronization of PROPELLER MRI and NIRSThe codes include the non-cartesian reconstruction for PROPELLER sequences, and the fundamental process for raw optical signals.
- Data for: Dr. Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantationThe data consist of a comparison of intraoperatively measured graft weight with preoperatively estimated graft weight by Dr. Liver and syngo.via in terms of accuracy and time efficiency.
- Supplementary Materials for: Supervised signal detection for adverse drug reactions in medication dispensing dataThis file compares potential signals of adverse drug reactions (ADRs) detected by sequence symmetry analysis (SSA) and supervised gradient boosting classifier. ADR signals of higher confidence are assigned higher adjusted sequence ratios (rightward) by SSA and higher probabilities (upward) by gradient boosting classifier. Blue circles represent known ADRs while red squares indicate unknown potential ADR signals. A signal is picked up by SSA if the 95% confidence interval lower limit of its adjusted sequence ratio exceeds 1 and picked up by gradient boosting classifier if its probability is greater than 0.5. ADR signals of higher confidence are assigned higher adjusted sequence ratios (rightward) by SSA and higher probabilities (upward) by gradient boosting classifier.
- Data for: Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels Segmentation Here, two free and online publicly available databases: STARE and DRIVE for retinal blood vessels
- Data for: Classification Model based on strain measurements to identify patients with Arrhythmogenic Cardiomyopathy with Left Ventricular Involvement DataBase.xls: PCA 1st component of the radial, circumferential and longitudinal strain for the main study and the follow-up study. strain.mat: data file with radial, circumferential and longitudinal strain values for the 16 AHA segments of the main and the follow-up studies.
- Data for: Accelerating B-spline Interpolation on GPUs: Application to Medical Image RegistrationThe dataset contains 5 pairs of images (for a total of 10 images). The images have been preprocessed, segmented and masked. Affine transformation has been applied across pre- and intra-operative pairs, i.e they are ready for non-rigid registration. There are 3 pairs of images of a liver phantom based on patient specific data. Deformation simulating pneumoperitoneum has been applied to these images. ARTORG centre and Cascination produced the phantom. They are x-ray reconstructions (DynaCT). Images 6 - 9 are image pairs of pre- and intra- operative porcine liver MRI scans conducted at Oslo University Hospital - Rikshospitalet, Norway. Intra-operative images have pneumoperitoneum applied to the abdomen. They are contrast-enhanced MR images (enhanced-T1 high-resolution isotropic volume examination (eTHRIVE)). The dataset was created at and owned by The Intervention Center, Oslo University Hospital - Rikshospitalet, Norway.
- SICAPv2 - Prostate Whole Slide Images with Gleason Grades AnnotationsA database containing prostate histology whole slide images with both annotations of global Gleason scores and path-level Gleason grades. Data associated with the paper: Silva-Rodríguez, J., Colomer, A., Sales, M. A., Molina, R., & Naranjo, V. (2020). Going deeper through the Gleason scoring scale : An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Computer Methods and Programs in Biomedicine, 195. https://doi.org/10.1016/j.cmpb.2020.105637
- Simulated data set, and its R script, which was used to obtain the results provided in this paper.Simulated data set, and its R script, which was used to obtain the results provided in this paper.
- Data for: Survival Estimation through the Cumulative Hazard with Monotone Natural Cubic Splines Using Convex Optimization-the HCNS approachThis files contains all codes, data as well as descriptions of its use accompanied with reproducible examples.
- Data for: surrosurv: an R Package for the Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses of Randomized Clinical TrialsR package surrosurv, containing R functions to evaluate failure time surrogate endpoints. The package contains data on two meta-analysis of (adjuvant) chemotherapy in gastric cancer.
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