A layer-level multi-scale architecture for lung cancer classification with fluorescence lifetime imaging endomicroscopy

In this paper, we introduce our unique dataset of fluorescence lifetime imaging endo/microscopy (FLIM), containing over 100,000 different FLIM images collected from 18 pairs of cancer/non-cancer human lung tissues of 18 patients by our custom fibre-based FLIM system. The aim of providing this datase...

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Bibliographic Details
Published in:Neural computing & applications 2022-11, Vol.34 (21), p.18881-18894
Main Authors: Wang, Qiang, Hopgood, James R., Fernandes, Susan, Finlayson, Neil, Williams, Gareth O. S., Akram, Ahsan R., Dhaliwal, Kevin, Vallejo, Marta
Format: Article
Language:English
Subjects:
Artificial Intelligence
Best practice
Cancer
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Configuration management
Data Mining and Knowledge Discovery
Datasets
Feature extraction
Fluorescence
Image Processing and Computer Vision
Lung cancer
Medical imaging
Original Article
Probability and Statistics in Computer Science
Scale models
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Summary:In this paper, we introduce our unique dataset of fluorescence lifetime imaging endo/microscopy (FLIM), containing over 100,000 different FLIM images collected from 18 pairs of cancer/non-cancer human lung tissues of 18 patients by our custom fibre-based FLIM system. The aim of providing this dataset is that more researchers from relevant fields can push forward this particular area of research. Afterwards, we describe the best practice of image post-processing suitable per the dataset. In addition, we propose a novel hierarchically aggregated multi-scale architecture to improve the binary classification performance of classic CNNs. The proposed model integrates the advantages of multi-scale feature extraction at different levels, where layer-wise global information is aggregated with branch-wise local information. We integrate the proposal, namely ResNetZ, into ResNet, and appraise it on the FLIM dataset. Since ResNetZ can be configured with a shortcut connection and the aggregations by Addition or Concatenation , we first evaluate the impact of different configurations on the performance. We thoroughly examine various ResNetZ variants to demonstrate the superiority. We also compare our model with a feature-level multi-scale model to illustrate the advantages and disadvantages of multi-scale architectures at different levels.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07481-1