Extraction and expression of architectural color. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Basic color value distribution map of the street. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

SegNet architecture. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Overview of workflow. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Descriptive statistics for the volunteers. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Jiefang North Road Street. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Colors with different number of clusters. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by Yuhong Huang (115702)

“…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …”

Image 1_Interplay between noise-induced sensorineural hearing loss and hypertension: pathophysiological mechanisms and therapeutic prospects.png by Carola Y. Förster (11595118)

DataSheet_1_MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study.docx by Fan Yang (1413)

“…A total of 968 radiomics features were extracted from preoperative multisequence MR images. The maximum relevance minimum redundancy algorithm was applied for feature selection. …”

Image_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

Image_5_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

Image_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

Image_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

Image_6_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

Image_4_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

Image 1_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

Image 2_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

Image 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

Image 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)