Vision-based Road Segmentation for Intelligent Vehicles using Deep Convolutional Neural Networks

Öztel, Gözde Yolcu

doi:10.1109/inista52262.2021.9548601

Cited by 2 publications

(1 citation statement)

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“…The incorporation of semantic segmentation in ITS [4]- [6] is a rapidly progressing domain, with continuous research and development initiatives that striving to enhance the precision, resilience, and efficiency of these algorithms. 1 Wei Zhao, Qiyu Wei, Zeng Zeng are with School of Microelectronics, Shanghai University (SHU), China, 201800 (Email: zw1315753898, qywei@shu.edu.cn, zeng zeng@hotmail.com)…”

Section: Introductionmentioning

confidence: 99%

Deeply Supervised Active Learning for Finger Bones Segmentation

Zhao

Yang

Veeravalli

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation. Clinical relevanceThe proposed method only needs a few annotated samples on the finger bones task to achieve comparable results in comparison with full annotation, which can be used to segment finger bones for medical practices, and generalized into other clinical applications.

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Section: Introductionmentioning

confidence: 99%