Search Results for author:
Found 16 papers, 3 papers with code
DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks
Deep Neural Networks are widely used in academy as well as corporate and public applications, including safety critical applications such as health care and autonomous driving.
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models
In this work, we investigate the potential of large language models to generate patient summaries based on doctors' notes and study the effect of training data on the faithfulness and quality of the generated summaries.
Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer
Phacoemulsification cataract surgery (PCS) is a routine procedure conducted using a surgical microscope, heavily reliant on the skill of the ophthalmologist.
Building Blocks for a Complex-Valued Transformer Architecture
We test on a classification and a sequence generation task on the MusicNet dataset and show improved robustness to overfitting while maintaining on-par performance when compared to the real-valued transformer architecture.
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
We study the application of large language models to zero-shot and few-shot classification of tabular data.
Kernel-Based Generalized Median Computation for Consensus Learning
This framework computes the relationship between objects and its generalized median in kernel space, without the need of an explicit embedding.
A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation
One well established method of interactive image segmentation is the random walker algorithm.
A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods
As the proposed nested sweeps algorithm is fast, it can be used to generate test data on demand.
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images
First, the Subject-Shared module in CS-DASA receives multi-frame EEG image data from both source and target subjects and learns the common feature representations.
Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images
The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field.
Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets
Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes.
The PHOTON Wizard -- Towards Educational Machine Learning Code Generators
Despite the tremendous efforts to democratize machine learning, especially in applied-science, the application is still often hampered by the lack of coding skills.
PHOTONAI -- A Python API for Rapid Machine Learning Model Development
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development.
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression
We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies.
Biological sex classification with structural MRI data shows increased misclassification in transgender women
Subsequently, the classifier was applied to $N = 26$ TWs.
Barista - a Graphical Tool for Designing and Training Deep Neural Networks
In recent years, the importance of deep learning has significantly increased in pattern recognition, computer vision, and artificial intelligence research, as well as in industry.
