Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging

no code implementations • 5 Dec 2023 • Alexander Ziller, Tamara T. Mueller, Simon Stieger, Leonhard Feiner, Johannes Brandt, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models.

Anatomy-Driven Pathology Detection on Chest X-rays

1 code implementation • 5 Sep 2023 • Philip Müller, Felix Meissen, Johannes Brandt, Georgios Kaissis, Daniel Rueckert

Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions.

Anatomy Multiple Instance Learning +2

Interpretable 2D Vision Models for 3D Medical Images

1 code implementation • 13 Jul 2023 • Alexander Ziller, Ayhan Can Erdur, Marwa Trigui, Alp Güvenir, Tamara T. Mueller, Philip Müller, Friederike Jungmann, Johannes Brandt, Jan Peeken, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success.