1

AUTOMATIC SEGMENTATION USING DEEP LEARNING TO DISTINGUISH BETWEEN 9 TYPES OF SOFT-TISSUE TUMORS WITH RADIOMICS
The effect of preprocessing on convolutional neurnal networks for medical image segmentation
Differential diagnosis and mutation stratification of gastrointestinal stromal tumours on CT images using a radiomics approach
Distinguishing desmoid-type fibromatosis from soft tissue sarcoma on MRI using a radiomics approach
Distinguishing well-differentiated liposarcomas from lipomas on MR images using a radiomics approach
Prediction of histopathological growth patterns by radiomics and CT-imaging in patients with operable colorectal liver metastases: a proof-of-concept study
Radiomics model to predict hepatocellular carcinoma on liver MRI of high-risk patients in surveillance: a proof-of-concept study
Radiomics of Gastrointestinal Stromal Tumors; Risk Classification Based on Computed Tomography Images – A Pilot Study
Fully automatic construction of optimal radiomics workflows
Differentiating well-differentiated liposarcomas from lipomas using a radiomics approach