Computing the centerline from a binary segmentation. by Patrick D. McClanahan (2385076)

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

Table_2_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.docx by Bettina Katalin Budai (13951317)

Table_1_Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.DOCX by Bettina Katalin Budai (13951317)

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)

Min-Cut/Max-Flow Problem Instances for Benchmarking by Patrick Møller Jensen (7232042)

“…</p> </li><li> <p><strong>Oriented MRF segmentation</strong> based on [B04], [R21], [E14].</p> </li><li> <p><strong>U-Net segmentation cleaning with MRFs</strong> based on [B04] “An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision”, 2004, PAMI:</p> <ul><li> <p><strong>Cleaning of V-Net segmentations</strong> based on [R21], [M16], [E14].…”

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 experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. …”

Image3_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”

Image4_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”

Image1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”

Table1_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.docx by Varun Sendilraj (19732510)

“…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”

Image2_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”

Image5_DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.jpeg by Varun Sendilraj (19732510)

“…The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. …”

<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. …”