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Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images | IEEE Journals & Magazine | IEEE Xplore

Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images


Abstract:

Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscop...Show More

Abstract:

Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of inadequate labeled samples. Therefore, for end-to-end medical image detection with overcritical efficiency and accuracy in endoscope detection, an ensemble-learning-based model with a semi-supervised mechanism is developed in this work. To gain a more accurate result through multiple detection models, we propose a new ensemble mechanism, termed alternative adaptive boosting method (Al-Adaboost), combining the decision-making of two hierarchical models. Specifically, the proposal consists of two modules. One is a local region proposal model with attentive temporal–spatial pathways for bounding box regression and classification, and the other one is a recurrent attention model (RAM) to provide more precise inferences for further classification according to the regression result. The proposal Al-Adaboost will adjust the weights of labeled samples and the two classifiers adaptively, and the nonlabel samples are assigned pseudolabels by our model. We investigate the performance of Al-Adaboost on both the colonoscopy and laryngoscopy data coming from CVC-ClinicDB and the affiliated hospital of Kaohsiung Medical University. The experimental results prove the feasibility and superiority of our model.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 36, Issue: 1, January 2025)
Page(s): 237 - 248
Date of Publication: 20 June 2023

ISSN Information:

PubMed ID: 37339032

Funding Agency:


References

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