Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach.
Cutaneous melanoma is a highly lethal form of cancer. Developing a medical image segmentation model capable of accurately delineating melanoma lesions with high robustness and generalization presents a formidable challenge. This study draws inspiration from cellular functional characteristics and na...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 1147 - 1165 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas pictorial research Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081754&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081754 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081754 184081754 184081754 10.1007/s10278-024-01257-w 184081754 ppf: 1147 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach. aug: au: Yang, Tongxin Huang, Qilin Cai, Fenglin Li, Jie Jiang, Li Xia, Yulong affil: https://ror.org/03n3v6d52 Chongqing University of Science and Technology, 401331, Chongqing, China sug: subj: Melanoma Pathology Neural Networks (Computer) Image Interpretation, Computer Assisted Image Processing, Computer Assisted Melanoma Radiography Skin Neoplasms Pathology Deep Learning Algorithms Melanoma Diagnosis Human Funding Source Models, Theoretical Apoptosis Cell Physiology Connective Tissue Anatomy and Histology Sensitivity and Specificity Cell Division Molecular Structure Memory Adaptation, Physiological Experimental Studies Ablation Techniques Neurons ab: Cutaneous melanoma is a highly lethal form of cancer. Developing a medical image segmentation model capable of accurately delineating melanoma lesions with high robustness and generalization presents a formidable challenge. This study draws inspiration from cellular functional characteristics and natural selection, proposing a novel medical segmentation model named the vital characteristics cellular neural network. This model incorporates vital characteristics observed in multicellular organisms, including memory, adaptation, apoptosis, and division. Memory module enables the network to rapidly adapt to input data during the early stages of training, accelerating model convergence. Adaptation module allows neurons to select the appropriate activation function based on varying environmental conditions. Apoptosis module reduces the risk of overfitting by pruning neurons with low activation values. Division module enhances the network's learning capacity by duplicating neurons with high activation values. Experimental evaluations demonstrate the efficacy of this model in enhancing the performance of neural networks for medical image segmentation. The proposed method achieves outstanding results across numerous publicly available datasets, indicating its potential to contribute significantly to the field of medical image analysis and facilitating accurate and efficient segmentation of medical imagery. The proposed method achieves outstanding results across numerous publicly available datasets, with an F1 score of 0.901, Intersection over Union of 0.841, and Dice coefficient of 0.913, indicating its potential to contribute significantly to the field of medical image analysis and facilitating accurate and efficient segmentation of medical imagery. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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