Visualization of the image segmentation and labeling pipeline for the first frame of an example movie. by Miriam Schnitzerlein (21446136)

The steps of proposed method of RWMA detection using echocardiography images for RWMA detection, consisting with cardiac structure segmentation using a trained U-Net on both A4C an... by Sazzli Kasim (11218558)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…<p dir="ltr">The first algorithm for segmentation and localization (see PathOlOgics_script_1; segment & localize using a pen) relied on manually tracing the borders of each cell using a digital pen tool on a big touchscreen display showing source images/patches. …”

3D Microvascular Image Data and Labels for Machine Learning by Natalie Holroyd (7099391)

“…The dataset contains images from a variety of imaging modalities, at different resolutions, using difference sources of contrast and featuring different organs/ pathologies. This data was use to train, test and validated a foundational model for 3D vessel segmentation, tUbeNet, which can be found on <a href="https://github.com/natalie11/tUbeNet" rel="noreferrer" target="_blank">github</a>. …”

Adaptive Inference for Change Points in High-Dimensional Data by Yangfan Zhang (6451946)

“…On the estimation front, we obtain the convergence rate of the maximizer of our test statistic standardized by sample size when there is one change-point in mean and <i>q</i> = 2, and propose to combine our tests with a wild binary segmentation algorithm to estimate the change-point number and locations when there are multiple change-points. …”

Key steps in neurodegeneration detection algorithm. by Andrew S. Clark (16510496)

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

“…., nonelectrolyte, electrolyte, and solvate) in single and mixed solvents using a symmetrically reformulated electrolyte nonrandom two-liquid segment activity coefficient (eNRTL-SAC) model. The methodology incorporates key features of the symmetric eNRTL-SAC model structure to reduce the number of parameters and uses a hybrid of global search algorithms for parameter estimation. …”

Flow chart of the algorithm. by Mukesh Karunanethy (18422857)

“…<p>Flow chart showing the algorithm pipeline, including time series normalization, filtering, feature extraction, feature reduction, and data splitting into training and testing. …”

DataSheet_1_Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer.docx by Shima Sepehri (11574997)

Fig 1 - by Monjoy Saha (12239792)

<b>Multimodal MRI radiomics</b><b> based on </b><b>habitat subregions of the tumor microenvironment</b><b> for predicting risk stratification in glioblastoma</b> by Han Wang (21457334)

“…</p><p dir="ltr">A fully automated approach involving label fusion from multiple deep learning algorithms was used to segment distinct tumor subregions histologically. …”

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

Data_Sheet_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.XLSX by Bettina Katalin Budai (13951317)