Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis

Bingsheng Huang, Junru Tian, Hongyuan Zhang, Zixin Luo, Jing Qin, Chen Huang, Xueping He, Yanji Luo, Yongjin Zhou, Guo Dan, Hanwei Chen, Shi Ting Feng, Chenglang Yuan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)

Abstract

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrated system. Although this combination is efficient in some tasks, the deep learning-based classification network is often difficult to capture an effective representation of lesion regions, and prone to face the challenge of overfitting, leading to unreliable features and inaccurate results, especially when the sizes of the lesions are small or the training dataset is small. In addition, these combinations mostly lack an effective feature selection mechanism, which makes it difficult to obtain the optimal feature selection. In this paper, we introduce a novel and effective deep semantic segmentation feature-based radiomics (DSFR) framework to overcome the above-mentioned challenges, which consists of two modules: the deep semantic feature extraction module and the feature selection module. Specifically, the extraction module is utilized to extract hierarchical semantic features of the lesions from a trained segmentation network. The feature selection module aims to select the most representative features by using a novel feature similarity adaptation algorithm. Experiments are extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs), and the prediction of thrombolytic therapy efficacy in deep venous thrombosis (DVT). Experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.

Original languageEnglish
Article number9286475
Pages (from-to)2655-2664
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number7
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Deep learning
  • Feature aggregation
  • Radiomics
  • Semantic features

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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