Correlation matters

multi-scale fine-grained contextual information extraction for hepatic tumor segmentation

Shuchao Pang*, Anan Du, Zhenmei Yu, Mehmet Orgun

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Automatic tumor segmentation has been used as a diagnostic aid in the identification of diseases such as tumors from liver CT scans, and their treatment. Owing to their success in computer vision tasks, the state-of-the-art Fully Convolutional Networks (FCNs) or U-Net based models have often been employed in many recent studies for automatic tumor segmentation to learn numerous weight-shared convolutional kernels and extract various semantic features. However, the correlation between different tumor regions in feature maps cannot be easily captured due to the lack of contextual dependencies, which in turn limits the representative capability of the adopted models and thus affects the accuracy of tumor segmentation results. To resolve this issue, we propose a novel framework for segmentation of tumors in liver CT scans, which can explicitly extract multi-scale fine-grained contextual information by adaptively aggregating local features with their global dependencies. The proposed multi-scale framework features a light model with a very few additional parameters, and also its visualization capability significantly boosts networks’ interpretability. Experimental results on a real-world liver tumor CT dataset illustrate that the proposed framework achieves the state-of-the-art performance in terms of a number of widely used evaluation criteria for the hepatic tumor segmentation task.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication24th Pacific-Asia Conference, PAKDD 2020 Singapore, May 11-15, 2020, Proceedings
EditorsHady W. Lauw, Raymond Chi-Wing Wong, Alexandros Nitoulas, Ee-Peng Lim, See-Kiong Ng, Sinno Jialin Pan
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages462-474
Number of pages13
ISBN (Electronic)9783030474263
ISBN (Print)9783030474256
DOIs
Publication statusPublished - 2020
Event24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore
Duration: 11 May 202014 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer, Springer Nature
Number1
Volume12084 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
CountrySingapore
CitySingapore
Period11/05/2014/05/20

Keywords

  • Hepatic tumor segmentation
  • Contextual information
  • Visualization
  • FCNs

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  • Cite this

    Pang, S., Du, A., Yu, Z., & Orgun, M. (2020). Correlation matters: multi-scale fine-grained contextual information extraction for hepatic tumor segmentation. In H. W. Lauw, R. C-W. Wong, A. Nitoulas, E-P. Lim, S-K. Ng, & S. J. Pan (Eds.), Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020 Singapore, May 11-15, 2020, Proceedings (pp. 462-474). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12084 LNAI, No. 1). Cham, Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-030-47426-3_36