11 Result(s)
within
Chapter
David Zimmerer
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Chapter and Conference Paper
Abstract: RecycleNet
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the ...
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Chapter and Conference Paper
Abstract: MOOD 2020
Detecting out-of-distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the di...
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Chapter and Conference Paper
Contrastive Representations for Unsupervised Anomaly Detection and Localization
Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring labels during training. Often, this is achieved by learning a data distribution of normal sam...
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Chapter and Conference Paper
Realistic Evaluation of FixMatch on Imbalanced Medical Image Classification Tasks
Semi-supervised learning offers great potential for medical image analysis, as it reduces the annotation burden for clinicians. In this work, we apply the state-of-the-art method FixMatch to chest X-ray and re...
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Chapter and Conference Paper
Unsupervised Anomaly Detection in the Wild
Unsupervised anomaly detection is often attributed great promise, especially for rare conditions and fast adaptation to novel conditions or imaging techniques without the need for explicitly labeled data. Howe...
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Chapter and Conference Paper
Continuous-Time Deep Glioma Growth Models
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has appro...
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Chapter and Conference Paper
Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have show...
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Chapter and Conference Paper
Unsupervised Anomaly Localization Using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have show...
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Chapter and Conference Paper
Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark in medical image segmentation. The adaptation of the U-Net to novel proble...
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Chapter and Conference Paper
Advanced Deep Learning Methods
The remarkable rise of deep learning has led to an overwhelming amount of new papers coming up by the week. This tutorial intents to filter out the research most relevant for the medical image computing (MIC) ...
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Chapter and Conference Paper
Spiking Convolutional Deep Belief Networks
Understanding visual input as perceived by humans is a challenging task for machines. Today, most successful methods work by learning features from static images. Based on classical artificial neural networks,...
