KEYWORDS: Medical imaging, Lymphatic system, Cancer, Computer aided diagnosis and therapy, Detection and tracking algorithms, Image processing, Databases, Data modeling, Data acquisition, Breast
It is commonplace for the lack of labeled data in novel domains on medical image computer-aided diagnosis but there
have been some labeled data or prior knowledge in old correlative domains. In this paper, instance-transfer approach is
introduced into medical image processing. And then we present a novel transfer learning model based on kernel
matching pursuit called TLKMP, which extends KMP (kernel matching pursuit learning machine, Vincent & Bengio,
2002). TLKMP uses the Greedy Approximation Residue to transfer instances into target domains which have little
labeled set different distributions from the source domains. So, valuable instances in resource domains are reused to
construct high quality classification model for the unlabeled set of the target domains. The experiment is performed
datasets on Gastric Cancer of Lymph Node database which comes from some a hospital. The results show that the
proposed algorithm has better classification performance compared with traditional KMP methods, and it improves
diagnosis accuracy rate of medical images effectively the same as the algorithm need lest labeled data for training a good
classification model.
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