Search Results for author:
Found 16 papers, 12 papers with code
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.
Towards Automated COVID-19 Presence and Severity Classification
COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times.
MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data
Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms.
Standardized Medical Image Classification across Medical Disciplines
AUCMEDI is a Python-based framework for medical image classification.
Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP
Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processsing (NLP).
GERNERMED++: Transfer Learning in German Medical NLP
We present a statistical model for German medical natural language processing trained for named entity recognition (NER) as an open, publicly available model.
Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework
The NuCLS dataset contains over 220. 000 annotations of cell nuclei in breast cancers.
Towards a Guideline for Evaluation Metrics in Medical Image Segmentation
In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation.
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks
However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines.
MISeval: a Metric Library for Medical Image Segmentation Evaluation
Thus, we propose our open-source publicly available Python package MISeval: a metric library for Medical Image Segmentation Evaluation.
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework
Evaluation metrics (Classification-Report, macro f1-scores, Confusion-Matrices, ROC-Curves) of the individual folds and the ensembles show that the classifier works well.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
GERNERMED -- An Open German Medical NER Model
The current state of adoption of well-structured electronic health records and integration of digital methods for storing medical patient data in structured formats can often considered as inferior compared to the use of traditional, unstructured text based patient data documentation.
Assessing the Role of Random Forests in Medical Image Segmentation
Our results indicate that random forest approaches are a good alternative to deep convolutional neural networks and, thus, allow the usage of medical image segmentation without a GPU.
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide.
Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net
To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases.
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation.
