Confusion matrices of (a-b) 60:40 of TRAP/TESP and (c-d) 70:30 of TRAP/TESP. حسب Naif Almakayeel (16846021)

الموضوعات:

ICH recognition outcome of HPDL-MIAIHD technique on 60:40 of TRAP/TESP. حسب Naif Almakayeel (16846021)

الموضوعات:

Average of HPDL-MIAIHD methodology on 70:30 of TRAP/TESP. حسب Naif Almakayeel (16846021)

الموضوعات:

outcome of HPDL-MIAIHD technique with recent methods [11–12]. حسب Naif Almakayeel (16846021)

الموضوعات:

ICH recognition outcome of HPDL-MIAIHD methodology on 60:40 of TRAP/TESP. حسب Naif Almakayeel (16846021)

الموضوعات:

curve of HPDL-MIAIHD methodology on 60:40 of TRAP/TESP. حسب Naif Almakayeel (16846021)

الموضوعات:

outcome of HPDL-MIAIHD technique with recent methods. حسب Naif Almakayeel (16846021)

الموضوعات:

Average of HPDL-MIAIHD technique on 60:40 of TRAP/TESP. حسب Naif Almakayeel (16846021)

الموضوعات:

PR curve of HPDL-MIAIHD method on 60:40 of TRAP/TESP. حسب Naif Almakayeel (16846021)

الموضوعات:

Comparative analysis of four distinct microstructures: S1G0, S1G1, S1G2, and S2G0. حسب Camilo A. S. Afanador (22232423)

"…The porosity of <b>S1G0</b> and <b>S2G0</b> () aligns with <i>μ</i>CT measurements from the literature. Note the increase in porosity in <b>S1G1</b> and <b>S1G2</b> due to the morphological filter. …"

Image 1_Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.tif حسب Junyi Lin (10573039)

Table 2_Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.docx حسب Junyi Lin (10573039)

Table 1_Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.docx حسب Junyi Lin (10573039)

Data Sheet 1_Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.csv حسب Junyi Lin (10573039)

Table 3_Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.xlsx حسب Junyi Lin (10573039)

Image 2_Combining clinical characteristics with CT radiomics to predict Ki67 expression level of small renal mass based on artificial intelligence algorithms.tif حسب Junyi Lin (10573039)

Data Sheet 1_Contrast quality control for segmentation task based on deep learning models—Application to stroke lesion in CT imaging.pdf حسب Juliette Moreau (2233273)

"…</p>Results<p>Application of this method to brain lesion segmentation in CT imaging highlights a Fisher's ratio threshold value of 0.05, and training validation of a new model without these images confirms this with similar results with only 60% of the training data, resulting in an almost 30% reduction in initial training time. …"

Data Sheet 1_Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.csv حسب Mustafa Çağlar (22611452)

"…The mean SSIM value was 0.763 ± 0.040 for CBCT-CT matches, 0.840 ± 0.026 with U-Net and 0.851 ± 0.026 with ResU-Net, with a significant increase in both models (p < 0.05). …"

Image 1_Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.tiff حسب Mustafa Çağlar (22611452)

"…The mean SSIM value was 0.763 ± 0.040 for CBCT-CT matches, 0.840 ± 0.026 with U-Net and 0.851 ± 0.026 with ResU-Net, with a significant increase in both models (p < 0.05). …"

Cut-off values. حسب Midori Miyagi (20455045)

"…Based on chest computed tomography (CT) findings we then defined patients with T4 (MI) and pectoralis (PMI) muscle indexes, L1 attenuation, and T4MI, PMI, and L1 attenuation below the cutoff values as having sarcopenia, osteoporosis, and osteosarcopenia, respectively.…"