The usage of Multi Instance Learning (MIL) for classifying Whole Slide Images (WSIs) has recently increased. Due to their gigapixel size, the pixel-level annotation of such data is extremely expensive and time-consuming, practically unfeasible. For this reason, multiple automatic approaches have been raised in the last years to support clinical practice and diagnosis. Unfortunately, most state-of-the-art proposals apply attention mechanisms without considering the spatial instance correlation and usually work on a single-scale resolution. To leverage the full potential of pyramidal structured WSI, we propose a graph-based multi-scale MIL approach, DAS-MIL. Our model comprises three modules: i) a self-supervised feature extractor, ii) a graph-based architecture that precedes the MIL mechanism and aims at creating a more contextualized representation of the WSI structure by considering the mutual (spatial) instance correlation both inter and intra-scale. Finally, iii) a (self) distillation loss between resolutions is introduced to compensate for their informative gap and significantly improve the final prediction. The effectiveness of the proposed framework is demonstrated on two well-known datasets, where we outperform SOTA on WSI classification, gaining a +2.7% AUC and +3.7% accuracy on the popular Camelyon16 benchmark.

A Graph-Based Multi-Scale Approach with Knowledge Distillation for WSI Classification / Bontempo, Gianpaolo; Bolelli, Federico; Porrello, Angelo; Calderara, Simone; Ficarra, Elisa. - In: IEEE TRANSACTIONS ON MEDICAL IMAGING. - ISSN 0278-0062. - 43:4(2024), pp. 1412-1421. [10.1109/TMI.2023.3337549]

A Graph-Based Multi-Scale Approach with Knowledge Distillation for WSI Classification

Gianpaolo Bontempo;Federico Bolelli;Angelo Porrello;Simone Calderara;Elisa Ficarra
2024

Abstract

The usage of Multi Instance Learning (MIL) for classifying Whole Slide Images (WSIs) has recently increased. Due to their gigapixel size, the pixel-level annotation of such data is extremely expensive and time-consuming, practically unfeasible. For this reason, multiple automatic approaches have been raised in the last years to support clinical practice and diagnosis. Unfortunately, most state-of-the-art proposals apply attention mechanisms without considering the spatial instance correlation and usually work on a single-scale resolution. To leverage the full potential of pyramidal structured WSI, we propose a graph-based multi-scale MIL approach, DAS-MIL. Our model comprises three modules: i) a self-supervised feature extractor, ii) a graph-based architecture that precedes the MIL mechanism and aims at creating a more contextualized representation of the WSI structure by considering the mutual (spatial) instance correlation both inter and intra-scale. Finally, iii) a (self) distillation loss between resolutions is introduced to compensate for their informative gap and significantly improve the final prediction. The effectiveness of the proposed framework is demonstrated on two well-known datasets, where we outperform SOTA on WSI classification, gaining a +2.7% AUC and +3.7% accuracy on the popular Camelyon16 benchmark.
2024
20-nov-2023
43
4
1412
1421
A Graph-Based Multi-Scale Approach with Knowledge Distillation for WSI Classification / Bontempo, Gianpaolo; Bolelli, Federico; Porrello, Angelo; Calderara, Simone; Ficarra, Elisa. - In: IEEE TRANSACTIONS ON MEDICAL IMAGING. - ISSN 0278-0062. - 43:4(2024), pp. 1412-1421. [10.1109/TMI.2023.3337549]
Bontempo, Gianpaolo; Bolelli, Federico; Porrello, Angelo; Calderara, Simone; Ficarra, Elisa
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1327126
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