Dr. Maria Vakalopoulou, Assistant professor, University Paris Saclay, France

Title: Computational Methods for more accurate and precise digital pathology processing

Abstract: In recent years, the medical research community has devoted significant attention to developing new methods for processing digital pathology slides. In this talk, I will present several novel approaches, benchmarks, and analyses that our group has been working on in this area the last year. I will begin by introducing an efficient and comprehensive benchmark we have developed to evaluate and compare foundation models across a variety of tasks and robustness settings [1]. Next, I will discuss our recent paper proposing a new method for efficient data augmentation in a multi-instance learning framework [2]. Finally, I will outline additional strategies for improving the performance of multi-instance learning models for various clinical endpoints [3].

1. Marza, Pierre, Leo Fillioux, Sofiène Boutaj, Kunal Mahatha, Christian Desrosiers, Pablo Piantanida, Jose Dolz, Stergios Christodoulidis, and Maria Vakalopoulou. "THUNDER: Tile-level Histopathology image UNDERstanding benchmark.NeuRIPS 2025 Benchmark and Dataset track (Spotlight)

2. Boutaj, Sofiène, Marin Scalbert, Pierre Marza, Florent Couzinie-Devy, Maria Vakalopoulou, and Stergios Christodoulidis. "Controllable Latent Space Augmentation for Digital Pathology." ICCV (2025)

3. Lolos, Andreas, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz, and Aris Moustakas. "SGPMIL: Sparse Gaussian Process Multiple Instance Learning." arXiv preprint arXiv:2507.08711 (2025).