Effect of image resolution on the performance of the binary classifier algorithm in predicting occurrence of a bird target(s) on an individual image using the validation imagery se... by Tsung-Wei Ke (18297551)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Cell classification. by Adam L. Tyson (10892822)

Results of the model on test sets 1 and 2. by Rémy Peyret (14691736)

“…We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. …”

Data set constituents. by Rémy Peyret (14691736)

“…We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. …”

Scanners and staining methods. by Rémy Peyret (14691736)

“…We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. …”

Symmetry test result. by Jiyong Moon (16819302)

Detecting an insect with background subtraction. by Malika Nisal Ratnayake (10131402)

Table_1_Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.docx by Hyo-jae Lee (11780051)

Table_1_Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.docx by Hyo-jae Lee (11780051)

Image_1_Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.pdf by Konrad Stawiski (4753380)

WEISS Catheter Segmentation in Fluoroscopy Dataset by Evangelos Mazomenos (6761597)

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

Recognition of each neuron’s digital identity. by Yuxiang Wu (748543)

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)

“…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. …”

Dataset selection process and exclusion criteria. by Seung Seog Han (4782486)

DataSheet_1_Histopathology image classification: highlighting the gap between manual analysis and AI automation.pdf by Refika Sultan Doğan (17799677)

Flow diagram of the automatic animal detection and background reconstruction. by David Tadres (9120564)

“…If the identical blob that was detected in panel J (bottom) is found in any of the new subtracted binary images (cyan arrow), the animal is considered as having left its original position, and the algorithm continues. …”

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

“…The network predicts a probability of being a neuron, a bounding box and a binary mask for each candidate neuron taking summary images as inputs (mean and correlation). …”

Fig 1 - by Monjoy Saha (12239792)

“…To restore the missing parts, we applied the convex hull algorithm to the binary mask. The convex hull algorithm detects convex points [red dots in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0263916#pone.0263916.g001" target="_blank">Fig 1C</a>], and we compute pairwise Euclidean distances to connect nearest-neighbor points by a green line if the distance between them is less than 80 pixels [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0263916#pone.0263916.g001" target="_blank">Fig 1C</a>]. …”