Search Results for author: Anjany Sekuboyina

Found 24 papers, 10 papers with code

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

1 code implementation30 Aug 2021 Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze

Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.

Graph Learning

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

Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection

no code implementations18 Aug 2020 Malek Husseini, Anjany Sekuboyina, Maximilian Loeffler, Fernando Navarro, Bjoern H. Menze, Jan S. Kirschke

Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme.

General Classification Representation Learning

Deep Reinforcement Learning for Organ Localization in CT

no code implementations MIDL 2019 Fernando Navarro, Anjany Sekuboyina, Diana Waldmannstetter, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze

Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional image analysis.

Image Retrieval reinforcement-learning

Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns

no code implementations25 Jan 2020 Hongwei Li, Timo Loehr, Anjany Sekuboyina, Jian-Guo Zhang, Benedikt Wiestler, Bjoern Menze

In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e. g. a new centreor a new scanner.

Lesion Segmentation Semantic Segmentation +1

Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis

no code implementations22 Jul 2019 Anjany Sekuboyina, Markus Rempfler, Alexander Valentinitsch, Maximilian Loeffler, Jan S. Kirschke, Bjoern H. Menze

We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision.

DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

1 code implementation29 Apr 2019 Hongwei Li, Johannes C. Paetzold, Anjany Sekuboyina, Florian Kofler, Jian-Guo Zhang, Jan S. Kirschke, Benedikt Wiestler, Bjoern Menze

Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).

Image Generation

Multi-level Activation for Segmentation of Hierarchically-nested Classes

no code implementations5 Apr 2018 Marie Piraud, Anjany Sekuboyina, Bjoern H. Menze

For many biological image segmentation tasks, including topological knowledge, such as the nesting of classes, can greatly improve results.

Multi-class Classification Semantic Segmentation

A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets

no code implementations13 Mar 2017 Anjany Sekuboyina, Alexander Valentinitsch, Jan S. Kirschke, Bjoern H. Menze

The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context.

Data Augmentation

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