Mtmr-net: Multi-task deep learning with margin ranking loss for lung nodule analysis

Lihao Liu, Qi Dou, Hao Chen, Iyiola E. Olatunji, Jing Qin, Pheng Ann Heng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

14 Citations (Scopus)

Abstract

Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis of lung nodules is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accurate lung nodule benign-malignant classification and attribute score grading. However, this is quite challenging due to the considerable difficulty of nodule heterogeneity modelling and limited discrimination capability on ambiguous cases. To meet these challenges, we propose a Multi-Task deep learning framework with a novel Margin Ranking loss (referred as MTMR-Net) for automated lung nodule analysis. The relatedness between lung nodule classification and attribute score regression is explicitly explored in our multi-task model, which can contribute to the performance gains of both tasks. The results of different tasks can be yielded simultaneously for assisting the radiologists in diagnosis interpretation. Furthermore, a siamese network with a novel margin ranking loss was elaborately designed to enhance the discrimination capability on ambiguous nodule cases. We validated the efficacy of our MTMR-Net on the public benchmark LIDC-IDRI dataset. Extensive experiments demonstrated that our approach achieved competitive classification performance and more accurate attribute scoring over the state-of-the-arts.

Original languageEnglish
Title of host publicationDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018
EditorsLena Maier-Hein, Tanveer Syeda-Mahmood, Zeike Taylor, Zhi Lu, Danail Stoyanov, Anant Madabhushi, João Manuel R.S. Tavares, Jacinto C. Nascimento, Mehdi Moradi, Anne Martel, Joao Paulo Papa, Sailesh Conjeti, Vasileios Belagiannis, Hayit Greenspan, Gustavo Carneiro, Andrew Bradley
PublisherSpringer Verlag
Pages74-82
Number of pages9
ISBN (Print)9783030008888
DOIs
Publication statusPublished - Sep 2018
Event4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018 - Granada, Spain
Duration: 20 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11045 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018
Country/TerritorySpain
CityGranada
Period20/09/1820/09/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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