Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification

Takeshi Yoshida, Kazuki Uehara, Hidenori Sakanashi, Hidenori Sakanashi, Hirokazu Nosato, Masahiro Murakawa, Masahiro Murakawa

2023

Abstract

This study proposes a multi-scale attention assembler network (MSAA-Net) for multi-scale pathological image classification. The proposed method discovers crucial features by observing each scale and finding essential scales used for classification. To realize this characteristic, we introduce a two-stage feature aggregation mechanism, which first assigns the attention weights to useful local regions for each scale and then assigns the attention weights to the scale. The mechanism observes a pathological image from each scale perspective and adaptively determines the essential scale to classify from the observation results. To train the MSAA-Net, we adopt multiple instance learning (MIL), a learning approach for predicting a label corresponding to multiple images. The labeling effort reduces because the MIL trains the classification model using diagnoses for whole slide-level images obtained by daily diagnoses of pathologists instead of detailed annotations of the images. We conducted classification using two pathological image datasets to evaluate the proposed method. The results indicate that the proposed method outperforms state-of-the-art multi-scale-based methods.

Paper Citation

in Harvard Style

Yoshida T., Uehara K., Sakanashi H., Nosato H. and Murakawa M. (2023). Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 619-628. DOI: 10.5220/0011615200003411

in Bibtex Style

@conference{icpram23,
author={Takeshi Yoshida and Kazuki Uehara and Hidenori Sakanashi and Hirokazu Nosato and Masahiro Murakawa},
title={Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={619-628},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011615200003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Multi-Scale Feature Aggregation Based Multiple Instance Learning for Pathological Image Classification
SN - 978-989-758-626-2
AU - Yoshida T.
AU - Uehara K.
AU - Sakanashi H.
AU - Nosato H.
AU - Murakawa M.
PY - 2023
SP - 619
EP - 628
DO - 10.5220/0011615200003411