Deep learning-powered multiple-object segmentation for computer-aided diagnosis

Weiming Fan, Tianyu Ma, Hongwei Gao, Jiahui Yu, Zhaojie Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Recently, driven by hardware devices and deep learning technologies, computer-aided diagnosis systems have been widely applied, such as cancer diagnosis and early screening of autistic children. Many studies have reported extracting tumor regions from whole-slide images (WSI) in cancer diagnosis tasks, namely, image segmentation. However, doctors must re-analyze the ROI in the tumor area for some challenging diseases. Efficient segmentation algorithms are the key parts of perfecting machine diagnostic assistance systems. This paper presents a novel WSI segmentation framework (called UFINet), aiming to segment the tumor region on the liver tissue image and re-segment the region of interest in the tumor. The proposed algorithm provides a solution for applying medical human-computer interaction systems. The proposed framework was trained and tested on the liver tissue dataset and achieved a Dice of 66% on 86 WSIs. Experiments prove that the proposed UFIN et achieves top performance and meets the clinical requirements, providing an effective method for developing computer-aided diagnosis systems.
Original languageEnglish
Title of host publicationProceedings of 42nd Chinese Control Conference (CCC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789887581543
ISBN (Print)9798350342598
Publication statusPublished - 18 Sept 2023
Event42nd Chinese Control Conference (CCC) -
Duration: 24 Jul 202326 Jul 2023

Publication series

NameIEEE CCC Proceedings Series
ISSN (Electronic)1934-1768


Conference42nd Chinese Control Conference (CCC)


  • human-computer interaction
  • WSI
  • segmentation
  • deep learning

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