Enhancing PFI Prediction with GDS-MIL: A Graph-Based Dual Stream MIL Approach

Gianpaolo Bontempo, Nicola Bartolini, Marta Lovino, Federico Bolelli, Anni Virtanen, Elisa Ficarra

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Whole-Slide Images (WSI) are emerging as a promising resource for studying biological tissues, demonstrating a great potential in aiding cancer diagnosis and improving patient treatment. However, the manual pixel-level annotation of WSIs is extremely time-consuming and practically unfeasible in real-world scenarios. Multi-Instance Learning (MIL) have gained attention as a weakly supervised approach able to address lack of annotation tasks. MIL models aggregate patches (e.g., cropping of a WSI) into bag-level representations (e.g., WSI label), but neglect spatial information of the WSIs, crucial for histological analysis. In the High-Grade Serous Ovarian Cancer (HGSOC) context, spatial information is essential to predict a prognosis indicator (the Platinum-Free Interval, PFI) from WSIs. Such a prediction would bring highly valuable insights both for patient treatment and prognosis of chemotherapy resistance. Indeed, NeoAdjuvant ChemoTherapy (NACT) induces changes in tumor tissue morphology and composition, making the prediction of PFI from WSIs extremely challenging. In this paper, we propose GDS-MIL, a method that integrates a state-of-the-art MIL model with a Graph ATtention layer (GAT in short) to inject a local context into each instance before MIL aggregation. Our approach achieves a significant improvement in accuracy on the “Ome18” PFI dataset. In summary, this paper presents a novel solution for enhancing PFI prediction in HGSOC, with the potential of significantly improving treatment decisions and patient outcomes.

Original languageEnglish
Title of host publicationImage Analysis and Processing – ICIAP 2023 - 22nd International Conference, ICIAP 2023, Proceedings
EditorsGian Luca Foresti, Andrea Fusiello, Edwin Hancock
Number of pages13
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date2023
Pages550-562
ISBN (Print)978-3-031-43147-0
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Article in conference proceedings
EventInternational Conference on Image Analysis and Processing - Udine, Italy
Duration: 11 Sept 202315 Sept 2023
Conference number: 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14233 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of Science

  • 113 Computer and information sciences

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