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The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
Malignant melanoma (MM) is one of the deadliest types of skin cancer. An...
08/28/2020 ∙ by Amirreza Mahbod, et al. ∙
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Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
In medical imaging, technical progress or changes in diagnostic procedur...
07/06/2020 ∙ by Johannes Hofmanninger, et al. ∙
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Separation of target anatomical structure and occlusions in chest radiographs
Chest radiographs are commonly performed low-cost exams for screening an...
02/03/2020 ∙ by Johannes Hofmanninger, et al. ∙
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Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem
Automated segmentation of anatomical structures is a crucial step in man...
01/31/2020 ∙ by Johannes Hofmanninger, et al. ∙
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Unsupervised deep clustering for predictive texture pattern discovery in medical images
Predictive marker patterns in imaging data are a means to quantify disea...
01/31/2020 ∙ by Matthias Perkonigg, et al. ∙
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Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
Diagnosis and treatment guidance are aided by detecting relevant biomark...
05/29/2019 ∙ by Philipp Seeböck, et al. ∙
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Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Optical coherence tomography (OCT) has become the most important imaging...
01/24/2019 ∙ by Philipp Seeböck, et al. ∙
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Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
The identification and quantification of markers in medical images is cr...
10/31/2018 ∙ by Philipp Seeböck, et al. ∙
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Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse correspo...
06/22/2018 ∙ by Christian Wachinger, et al. ∙
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Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
The automatic detection of disease related entities in retinal imaging d...
05/08/2018 ∙ by Thomas Schlegl, et al. ∙
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Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progr...
03/17/2017 ∙ by Thomas Schlegl, et al. ∙
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NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
This workshop explores the interface between cognitive neuroscience and ...
01/06/2017 ∙ by Leila Wehbe, et al. ∙
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Identifying and Categorizing Anomalies in Retinal Imaging Data
The identification and quantification of markers in medical images is cr...
12/02/2016 ∙ by Philipp Seeböck, et al. ∙
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