What is it about?
Lesion detection technology based on deep learning requires huge amounts of high-quality medical image data, but the data from social IoT has the problems of uneven quality and lack of lesion labeling. This paper proposes a core data extraction method for multi-class lesion detection, to effectively reduce the labeling burden.
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Why is it important?
Compared with serial and parallel extraction of multiple single-class core data, our method greatly improves the extraction speed and reduces the resource waste of multi-model training. And our experimental results show that our method can effectively extract core data of multiple lesions from low-quality medical images, and realize higher accuracy and better interpretability.
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This page is a summary of: LesionTalk: Core Data Extraction and Multi-class Lesion Detection in IoT-based Intelligent Healthcare, ACM Transactions on Sensor Networks, March 2023, ACM (Association for Computing Machinery), DOI: 10.1145/3526194.
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