Adaptive feature aggregation network for nuclei segmentation

Ruizhe Geng, Zhongyi Huang, Jie Chen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nuclei instance segmentation is essential for cell morphometrics and analysis, playing a crucial role in digital pathology. The problem of variability in nuclei characteristics among diverse cell types makes this task more challenging. Recently, proposal-based segmentation methods with feature pyramid network (FPN) has shown good performance because FPN integrates multi-scale features with strong semantics. However, FPN has information loss of the highest-level feature map and sub-optimal feature fusion strategies. This paper proposes a proposal-based adaptive feature aggregation methods (AANet) to make full use of multi-scale features. Specifically, AANet consists of two components: Context Augmentation Module (CAM) and Feature Adaptive Selection Module (ASM). In feature fusion, CAM focus on exploring extensive contextual information and capturing discriminative semantics to reduce the information loss of feature map at the highest pyramid level. The enhanced features are then sent to ASM to get a combined feature representation adaptively over all feature levels for each RoI. The experiments show our model's effectiveness on two publicly available datasets: the Kaggle 2018 Data Science Bowl dataset and the Multi-Organ nuclei segmentation dataset.

Original languageEnglish
Title of host publicationProceedings of the 2nd ACM International Conference on Multimedia in Asia, MMAsia 2020
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450383080
DOIs
Publication statusPublished - 7 Mar 2021
Externally publishedYes
Event2nd ACM International Conference on Multimedia in Asia, MMAsia 2020 - Virtual, Online, Singapore
Duration: 7 Mar 2021 → …

Publication series

NameProceedings of the 2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

Conference

Conference2nd ACM International Conference on Multimedia in Asia, MMAsia 2020
Country/TerritorySingapore
CityVirtual, Online
Period7/03/21 → …

Keywords

  • histopathology images
  • neural networks
  • nuclei segmentation

Fingerprint

Dive into the research topics of 'Adaptive feature aggregation network for nuclei segmentation'. Together they form a unique fingerprint.

Cite this