46 Result(s)
within
Chapter
Georg Langs
Image Processing and Computer Vision
-
Chapter and Conference Paper
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition
Imaging in clinical routine is subject to changing scanner protocols, hardware, or policies in a typically heterogeneous set of acquisition hardware. Accuracy and reliability of deep learning models suffer fro...
-
Chapter and Conference Paper
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI,...
-
Chapter and Conference Paper
Dynamic Memory to Alleviate Catastrophic Forgetting in Continuous Learning Settings
In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance. Scanner manufacturer, reconstruction kernel, dose, other protocol specific settings o...
-
Chapter and Conference Paper
Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning
The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we pr...
-
Chapter and Conference Paper
Predicting Future Bone Infiltration Patterns in Multiple Myeloma
Multiple Myeloma (MM) is a bone marrow malignancy affecting the generation pathway of plasma cells and B-lymphocytes. It results in their uncontrolled proliferation and malignant transformation and ultimately ...
-
Chapter
Ethical and Privacy Aspects of Using Medical Image Data
This chapter describes the ethical and privacy aspects of using medical data in the context of the VISCERAL project. The project had as main goals the creation of a benchmark for organ segmentation, landmark d...
-
Chapter
Datasets Created in VISCERAL
In the VISCERAL project, several Gold Corpus datasets containing medical imaging data and corresponding manual expert annotations have been created. These datasets were used for training and evaluation of partici...
-
Chapter and Conference Paper
Assessing Reorganisation of Functional Connectivity in the Infant Brain
As maturation of neural networks continues throughout childhood, brain lesions insulting immature networks have different impact on brain function than lesions obtained after full network maturation. Thus, lon...
-
Chapter
Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark
Health providers currently construct their differential diagnosis for a given medical case most often based on textbook knowledge and clinical experience. Data mining of the large amount of medical records gen...
-
Chapter and Conference Paper
Map** Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates
Population level analysis of medical imaging data relies on finding spatial correspondence across individuals as a basis for local comparison of visual characteristics. Here, we describe and evaluate a framewo...
-
Chapter and Conference Paper
Multivariate Manifold Modelling of Functional Connectivity in Develo** Language Networks
There is an increasing consensus in the scientific and medical communtities that functional brain analysis should be conducted from a connectionist standpoint. Most connectivity studies to date rely on derived...
-
Chapter and Conference Paper
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known mark...
-
Chapter
Annotating Medical Image Data
This chapter describes the annotation of the medical image data that were used in the VISCERAL project. Annotation of regions in the 3D images is non-trivial, and tools need to be chosen to limit the manual wo...
-
Chapter and Conference Paper
Overview of the 2015 Workshop on Medical Computer Vision — Algorithms for Big Data (MCV 2015)
The 2015 workshop on medical computer vision (MCV): algorithms for big data took place in Munich, Germany, in connection with MICCAI (Medical Image Computing for Computer Assisted Intervention). It is the fift...
-
Chapter and Conference Paper
Unsupervised Identification of Clinically Relevant Clusters in Routine Imaging Data
A key question in learning from clinical routine imaging data is whether we can identify coherent patterns that re-occur across a population, and at the same time are linked to clinically relevant patient para...
-
Chapter and Conference Paper
Modeling Fetal Cortical Expansion Using Graph-Regularized Gompertz Models
Understanding patterns of brain development before birth is of both high clinical and scientific interest. However, despite advances in reconstruction methods, the challenging setting of in-utero imaging rende...
-
Chapter and Conference Paper
Creating a Large-Scale Silver Corpus from Multiple Algorithmic Segmentations
Currently, increasingly large medical imaging data sets become available for research and are analysed by a range of algorithms segmenting anatomical structures automatically and interactively. While they prov...
-
Chapter and Conference Paper
A Locally Linear Method for Enforcing Temporal Smoothness in Serial Image Registration
Deformation fields obtained from image registration are commonly used for deriving measurements of morphological changes between reference and follow-up images. As the underlying image matching problem is ill-...
-
Chapter and Conference Paper
Overview of the VISCERAL Retrieval Benchmark 2015
The results of the VISCERAL 3D case retrieval benchmark were presented during the Multimodal Retrieval in the Medical Domain (MRMD) 2015 workshop in Vienna, Austria on March 29, 2015. The main task for the par...
-
Chapter and Conference Paper
Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion
The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map func...
