We are very happy to share that Dr. Maria Vakalopoulou (Ass. Prof. at University Paris Saclay, France) will visit our department for an invited talk regarding “Computational methods for more accurate and precise digital pathology processing”.
Logistics:
📅 Date: Tuesday December-#2, 18:00pm (Greece time)
🏢 Location: Room 3.7 (3rd floor), Department of Informatics and Telematics, Harokopio University, 9 Omirou Str., 17778, Tavros
💻 (Capability for) remote connection: https://us06web.zoom.us/j/86832691108?pwd=atZPHcVGyBV3FGapGjvG6rnJAarrbm.1
🌍 Participation: The talk is open to the general public
Talk 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).