Search Results for author:
Found 34 papers, 5 papers with code
Towards a New Science of a Clinical Data Intelligence
We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i. e., with data from many patients and with complete patient information.
A Multimodal Dialogue System for Medical Decision Support inside Virtual Reality
We present a multimodal dialogue system that allows doctors to interact with a medical decision support system in virtual reality (VR).
Fine-tuning deep CNN models on specific MS COCO categories
Fine-tuning of a deep convolutional neural network (CNN) is often desired.
A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces
This paper provides an overview of prominent deep learning toolkits and, in particular, reports on recent publications that contributed open source software for implementing tasks that are common in intelligent user interfaces (IUI).
A categorisation and implementation of digital pen features for behaviour characterisation
In this paper we provide a categorisation and implementation of digital ink features for behaviour characterisation.
Interactive Cognitive Assessment Tools: A Case Study on Digital Pens for the Clinical Assessment of Dementia
Interactive cognitive assessment tools may be valuable for doctors and therapists to reduce costs and improve quality in healthcare systems.
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings
We accelerate the fine-tuning process of the generic model to the target domain.
A CNN toolbox for skin cancer classification
We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification.
Incremental Improvement of a Question Answering System by Re-ranking Answer Candidates using Machine Learning
Our contributions are: (1) we generate a QA training corpus starting from 877 answers from the customer care domain of T-Mobile Austria, (2) we implement a state-of-the-art QA pipeline using neural sentence embeddings that encode queries in the same space than the answer index, and (3) we evaluate the QA pipeline and our re-ranking approach using a separately provided test set.
The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report
However, the main contribution is a diagnostic and decision support system in dermatology for patients and doctors, an interactive deep learning system for differential diagnosis of malignant skin lesions.
A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task
The aim of ImageCLEFmed Caption task is to develop a system that automatically labels radiology images with relevant medical concepts.
An Attention Mechanism with Multiple Knowledge Sources for COVID-19 Detection from CT Images
Until now, Coronavirus SARS-CoV-2 has caused more than 850, 000 deaths and infected more than 27 million individuals in over 120 countries.
Minimizing false negative rate in melanoma detection and providing insight into the causes of classification
Our goal is to bridge human and machine intelligence in melanoma detection.
TATL: Task Agnostic Transfer Learning for Skin Attributes Detection
Existing skin attributes detection methods usually initialize with a pre-trained Imagenet network and then fine-tune on a medical target task.
A Case Study on Pros and Cons of Regular Expression Detection and Dependency Parsing for Negation Extraction from German Medical Documents. Technical Report
We describe our work on information extraction in medical documents written in German, especially detecting negations using an architecture based on the UIMA pipeline.
Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction
This paper investigates the problem of domain adaptation for diabetic retinopathy (DR) grading.
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance in crowded scenes or in wide spaces.
Interactive Machine Learning for Image Captioning
We propose an approach for interactive learning for an image captioning model.
A survey on improving NLP models with human explanations
Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data.
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment.
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop
This paper presents our project proposal for extracting biomedical information from German clinical narratives with limited amounts of annotations.
Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust
In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the classification.
A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models
Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas.
A Deep Generative Model for Interactive Data Annotation through Direct Manipulation in Latent Space
The impact of machine learning (ML) in many fields of application is constrained by lack of annotated data.
Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory
Interactive machine learning (IML) is a beneficial learning paradigm in cases of limited data availability, as human feedback is incrementally integrated into the training process.
Putting Humans in the Image Captioning Loop
Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited.
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images.
Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency
Ensuring the trustworthiness and interpretability of machine learning models is critical to their deployment in real-world applications.
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation
However, selecting data for annotation remains a challenging problem due to the limited information available on unseen data.
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation
Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging.
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
Contrary to prior work, we propose a novel 2D--3D aggregation mechanism based on a differentiable solver for the \emph{Fused Gromov-Wasserstein Barycenter} problem and the use of an efficient online conformer generation method based on distance geometry.
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project
While MedDeepCyleAL can be applied to any kind of image data, we have specifically applied it to ophthalmology data in this project.
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
