“Small-data” Patch-wise Multi-dimensional Output Deep-learning for Rare Cancer Diagnosis in MRI under Limited Sample-size Situation
Author:
Affiliation:
1. Tokyo Institute of Technology,BioMedical Artificial Intelligence (BMAI) Research Unit,Tokyo,Japan
2. National Cancer Center Hospital,Tokyo,Japan
Funder
JST-Mirai Program
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635254.pdf?arnumber=10635254
Reference19 articles.
1. Rare cancers are not so rare: The rare cancer burden in Europe
2. The effect of neoadjuvant chemotherapy on physical fitness and survival in patients undergoing oesophagogastric cancer surgery
3. Childhood soft tissue sarcomas incidence and survival in European children (1978–1997): Report from the Automated Childhood Cancer Information System project
4. A review of soft-tissue sarcomas: translation of biological advances into treatment measures
5. Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors
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