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.

Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification

no code implementations • 28 Oct 2019 • Phawis Thammasorn, Daniel Hippe, Wanpracha Chaovalitwongse, Matthew Spraker, Landon Wootton, Matthew Nyflot, Stephanie Combs, Jan Peeken, Eric Ford

Deep representation learning using triplet network for classification suffers from a lack of theoretical foundation and difficulty in tuning both the network and classifiers for performance.

Data Augmentation General Classification +3

Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation

1 code implementation • 14 Aug 2019 • Fernando Navarro, Suprosanna Shit, Ivan Ezhov, Johannes Paetzold, Andrei Gafita, Jan Peeken, Stephanie Combs, Bjoern Menze

Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.

Computed Tomography (CT) Image Retrieval +3