Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentation

TL;DR

This paper introduces a fuzzy rank-based late fusion method that combines three semantic segmentation models to improve cytology image segmentation accuracy, achieving higher MeanIoU scores than traditional fusion methods.

Contribution

The paper proposes a novel fuzzy-based late fusion technique for cytology image segmentation that outperforms traditional fusion rules on two datasets.

Findings

Achieved maximum MeanIoU of 84.27% on HErlev dataset.

Achieved maximum MeanIoU of 83.79% on JUCYT-v1 dataset.

Outperforms traditional fusion methods like average probability and geometric mean.

Abstract

Cytology image segmentation is quite challenging due to its complex cellular structure and multiple overlapping regions. On the other hand, for supervised machine learning techniques, we need a large amount of annotated data, which is costly. In recent years, late fusion techniques have given some promising performances in the field of image classification. In this paper, we have explored a fuzzy-based late fusion techniques for cytology image segmentation. This fusion rule integrates three traditional semantic segmentation models UNet, SegNet, and PSPNet. The technique is applied on two cytology image datasets, i.e., cervical cytology(HErlev) and breast cytology(JUCYT-v1) image datasets. We have achieved maximum MeanIoU score 84.27% and 83.79% on the HErlev dataset and JUCYT-v1 dataset after the proposed late fusion technique, respectively which are better than that of the traditional fusion rules such as average probability, geometric mean, Borda Count, etc. The codes of the proposed model are available on GitHub.