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DICOM-GLIOMA-SEG
This dataset contains DICOM-SEG (DSO) conversions of the Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection and Segmentation Labels and Radiomic Features...
CROWDS-CURE-2017
Many Cancers routinely identified by imaging haven’t yet benefited from recent advances in computer science. Approaches such as machine learning and deep learning can generate quantitative tumor 3D volumes, complex features and therapy-tracking temporal dynamics. However, cross-disciplinary researchers striving to develop new approaches often lack disease understanding or sufficient contacts within the medical community....
Collection Manager Plugin Page
TCIA Collection Manager Plugin TCIA Collection Manager is a set…
Cancer Moonshot Biobank (CMB) Imaging Page
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Download Supporting Data Page
Analysis results, clinical trial data and other non-image information are…
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Data Usage Policies and Restrictions Page
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BREAST-MRI-NACT-PILOT
This collection contains longitudinal DCE MRI studies of 64 patients undergoing neoadjuvant chemotherapy (NACT) for invasive breast cancer.
Patient population
This pilot study to investigate the use of serial DCE MRI examinations during neoadjuvant chemotherapy for invasive breast cancer recruited 68 patients with stage II or III locally advanced breast cancer enrolled between...
[…] between homogeneous and heterogeneous datasets: Generic incremental transfer learning approach […]
BREAST-CANCER-SCREENING-DBT
Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.
Here,...