The main workflow of the proposed algorithm. by Xin Li (51274)

Comparison of images before and after pre-processing: the original image (left) and after applying top-hat and bottom-hat filters (right). by Rinrada Tanthanathewin (18611171)

An example of a histogram of the normalized intensity of an OCT image. by Rinrada Tanthanathewin (18611171)

Examples of results from two different OCT images shown in the left and right columns. by Rinrada Tanthanathewin (18611171)

ILM (pink line) and RPE (green line) layers in a normal OCT image. by Rinrada Tanthanathewin (18611171)

An example of an OCT image of a DR case (left) and illustration of relevant parts (right): EX-AN blobs (yellow blobs) in the area of interest (a blue region), the ILM (a red line)... by Rinrada Tanthanathewin (18611171)

Qualitative performance examples of <i>CosegPP</i>’s Buckwheat with three cosegmentation methods (DeepCO³, MIG, Subdiscover) and one plant phenotyping segmentation metho... by Rubi Quiñones (11378540)

Qualitative performance examples of <i>CosegPP</i>’s Sunflower with three cosegmentation methods (DeepCO³, MIG, Subdiscover) and one plant phenotyping segmentation metho... by Rubi Quiñones (11378540)

Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

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

Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

“…Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. …”

Objective performance of fully-automated SLAVV software. by Samuel A. Mihelic (11539996)

Algorithm framework of this paper. by Xiaoqin Wu (470428)

“…This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. …”

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)

“…<p dir="ltr">These images and associated binary labels were collected from collaborators across multiple universities to serve as a diverse representation of biomedical images of vessel structures, for use in the training and validation of machine learning tools for vessel segmentation. …”

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

“…Using the structures obtained through binarizing these images, the algorithm detects which image contains the dendrites (Scale bar = 10 μm). …”

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)

Hyperparameters of the LSTM Model. by Ahmed M. Elshewey (21463867)

“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”

The AD-PSO-Guided WOA LSTM framework. by Ahmed M. Elshewey (21463867)

“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”

Prediction results of individual models. by Ahmed M. Elshewey (21463867)

“…The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. …”