Fig 1 - by Monjoy Saha (12239792)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…In the context of facies recovery using simulations, the task of optimal sampling is formalized and addressed using a maximum information extraction criterion. …”

Dense block structure. 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. …”

Structure diagram of a transition layer. 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. …”

Dense-U-net network structure. 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. …”

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. …”

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. …”

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

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)

“…After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. …”

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)

“…After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. …”

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)

“…After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. …”

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. …”