A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation.
IEEE/ACM Trans Comput Biol Bioinform
; 18(1): 40-52, 2021.
Article
en En
| MEDLINE
| ID: mdl-31905144
ABSTRACT
Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains. Given some target data, the performance of transfer learning is determined by the degree of relevance between the source and target domains. To achieve positive transfer and avoid negative transfer, a negative-transfer-resistant mechanism is proposed by computing the weight of transferred knowledge. Extracting a negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space (called NTR-FC-SCT) is proposed by integrating negative-transfer-resistant and maximum mean discrepancy (MMD) into the framework of fuzzy c-means clustering. Experimental results show that the proposed NTR-FC-SCT model outperformed several traditional non-transfer and related transfer clustering algorithms.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Encéfalo
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Interpretación de Imagen Asistida por Computador
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Tomografía Computarizada por Rayos X
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Análisis por Conglomerados
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Lógica Difusa
Límite:
Humans
Idioma:
En
Revista:
ACM Trans Comput Biol Bioinform
Asunto de la revista:
BIOLOGIA
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INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article