Search Results for author: Jens Kleesiek

Found 6 papers, 2 papers with code

AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

no code implementations6 Aug 2021 Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao, Jens Kleesiek, Alejandro F. Frangi, Jan Egger

The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if combined with a contrast agent, resulting in a CT angiography (CTA).

Computed Tomography (CT)

The Federated Tumor Segmentation (FeTS) Challenge

1 code implementation12 May 2021 Sarthak Pati, Ujjwal Baid, Maximilian Zenk, Brandon Edwards, Micah Sheller, G. Anthony Reina, Patrick Foley, Alexey Gruzdev, Jason Martin, Shadi Albarqouni, Yong Chen, Russell Taki Shinohara, Annika Reinke, David Zimmerer, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazer, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Lena Maier-Hein, Jens Kleesiek, Bjoern Menze, Klaus Maier-Hein, Spyridon Bakas

The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.

Brain Tumor Segmentation Federated Learning +1

A Relational-learning Perspective to Multi-label Chest X-ray Classification

no code implementations10 Mar 2021 Anjany Sekuboyina, Daniel Oñoro-Rubio, Jens Kleesiek, Brandon Malone

Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i. e. learning to map an image directly to its binary labels.

Classification General Classification +3

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