TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing

Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma, Ulas Bagci

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Colonoscopy is considered the most effective screening test to detect colorectal cancer (CRC) and its precursor lesions, i.e., polyps. However, the procedure experiences high miss rates due to polyp heterogeneity and inter-observer dependency. Hence, several deep learning powered systems have been proposed considering the criticality of polyp detection and segmentation in clinical practices. Despite achieving improved outcomes, the existing automated approaches are inefficient in attaining real-time processing speed. Moreover, they suffer from a significant performance drop when evaluated on inter-patient data, especially those collected from different centers. Therefore, we intend to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance. The proposed architecture, TransNetR, is an encoder-decoder network that consists of a pre-trained ResNet50 as the encoder, three decoder blocks, and an upsampling layer at the end of the network. TransNetR obtains a high dice coefficient of 0.8706 and a mean Intersection over union of 0.8016 and retains a real-time processing speed of 54.60 on the Kvasir-SEG dataset. Apart from this, the major contribution of the work lies in exploring the generalizability of the TransNetR by testing the proposed algorithm on the out-of-distribution (test distribution is unknown and different from training distribution) dataset. As a use case, we tested our proposed algorithm on the PolypGen (6 unique centers) dataset and two other popular polyp segmentation benchmarking datasets. We obtained state-of-the-art performance on all three datasets during out-of-distribution testing. The source code of TransNetR is publicly available at https://github.com/DebeshJha/TransNetR.

Original languageEnglish (US)
Pages (from-to)1372-1384
Number of pages13
JournalProceedings of Machine Learning Research
Volume227
StatePublished - 2023
Event6th International Conference on Medical Imaging with Deep Learning, MIDL 2023 - Nashville, United States
Duration: Jul 10 2023Jul 12 2023

Keywords

  • Out-of-distribution generalization
  • Out-of-distribution testing
  • Polyp segmentation
  • PolypGen
  • Residual network
  • Transformer

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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