Johann Frei's research while affiliated with Universität Augsburg and other places
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Publications (12)
Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processsing (NLP). In general, developing and applying new NLP pipelines in domain-specific contexts for tasks often requires custom designed datasets to address NLP tasks in supervised machine learning fashion. When op...
In the context of clinical trials and medical research medical text mining can provide broader insights for various research scenarios by tapping additional text data sources and extracting relevant information that is often exclusively present in unstructured fashion. Although various works for data like electronic health reports are available for...
We present a statistical model for German medical natural language processing trained for named entity recognition (NER) as an open, publicly available model. The work serves as a refined successor to our first GERNERMED model which is substantially outperformed by our work. We demonstrate the effectiveness of combining multiple techniques in order...
We present a perspective on platforms for code submission and automated evaluation in the context of university teaching. Due to the COVID-19 pandemic, such platforms have become an essential asset for remote courses and a reasonable standard for structured code submission concerning increasing numbers of students in computer sciences. Utilizing au...
Background
In-vivo MR-based high-resolution volumetric quantification methods of the endolymphatic hydrops (ELH) are highly dependent on a reliable segmentation of the inner ear's total fluid space (TFS). This study aimed to develop a novel open-source inner ear TFS segmentation approach using a dedicated deep learning (DL) model.Methods
The model...
Background
Data mining in the field of medical data analysis often needs to rely solely on the processing of unstructured data to retrieve relevant data. For German natural language processing, few open medical neural named entity recognition (NER) models have been published before this work. A major issue can be attributed to the lack of German tr...
We present a perspective on platforms for code submission and automated evaluation in the context of university teaching. Due to the COVID-19 pandemic, such platforms have become an essential asset for remote courses and a reasonable standard for structured code submission concerning increasing numbers of students in computer sciences. Utilizing au...
Recent advancements in natural language processing (NLP) have been achieved by the use of increasingly complex neural networks. In clinical context, NLP is a key technique to access highly relevant information from unstructured texts such as clinical notes. We evaluate the feasibility of training our neural model GERNERMED on annotated German train...
BACKGROUND
Data mining in the field of medical data analysis often needs to rely solely on processing of unstructured data to retrieve relevant data. For German NLP, no open medical neural named entity recognition (NER) model has been published prior to this work. A major issue can be attributed to the lack of German training data.
OBJECTIVE
We de...
We perform classification, ranking and mapping of body sway parameters from static posturography data of patients using recent machine-learning and data-mining techniques. Body sway is measured in 293 individuals with the clinical diagnoses of acute unilateral vestibulopathy (AVS, n = 49), distal sensory polyneuropathy (PNP, n = 12), anterior lobe...
Background:
Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by other researchers or clinicians. Even if developers publish their code and pre-trained mod...
Citations
... 3D-quantification of the ELS consisted of three steps: first, segmentation of the total fluid space (TFS) was based on IE-Vnet [50], a recently proposed and pre-trained volumetric deep learning algorithm with V-net architecture that was deployed via the TOMAAT module [51] in a 3D-Slicer toolbox (version 4.11 [36]). Second, ELS and perilymphatic space (PS) were differentiated within the TFS using Volumetric Local Thresholding (VOLT; [52]) with ImageJ Fiji [53], the "Fuzzy and artificial neural networks image processing toolbox" [54], and the "MorphoLibJ Toolbox" [55]. ...
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... [Editor1.1] Recently, deep language models, like bidirectional encoder representations from transformers (BERT) 12 , have shown an impressive performance boost for various NLP downstream tasks in the German clinical domain, such as (i) information extraction from radiological reports [13][14][15][16] , (ii) free-text report classification 17,18 and (iii) oncology report summarization 19 . However, training large language models requires a significant amount of well-annotated training and testing data. ...
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... Generally static posture is predominated by ankle strategy [2][3][4]. In case of external or internal perturbation, postural sway always shifts toward high frequencies [4][5][6][7][8][9][10], indicating activation of hip strategy. Impaired hip strategy would jeopardize postural response adaption and lead to posture instability [11,12]. ...
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... It provides a cloud environment for general medical image analysis composed of three basic components: an announcement service, multiple distributed server nodes offering medical image analysis solutions, and client software offering simple user interfaces. TOMAAT requires a Python environment to work (Milletari et al., 2019). CVNodes is a high-level abstracted interface that leverages the low-level power of OpenCV and thus provides access to general image processing and vision algorithms that are applicable to many domains. ...
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