145 citations · 258 across the 10 of their papers we have counts for
10 papers
SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees
Sven Koitka, Giulia Baldini, Cynthia S. Schmidt +11
Traditional segmentation networks approach anatomical structures as standalone elements, overlooking the intrinsic hierarchical connections among them. This study introduces Softma…
ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset
Johannes Rückert, Louise Bloch, Raphael Brüngel +11
Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper intro…
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer +17
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries
Frederic Jonske, Moon Kim, Enrico Nasca +8
It is an open secret that ImageNet is treated as the panacea of pretraining. Particularly in medical machine learning, models not trained from scratch are often finetuned based on…
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez +24
Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload o…
Open-Source Skull Reconstruction with MONAI
Jianning Li, André Ferreira, Behrus Puladi +7
We present a deep learning-based approach for skull reconstruction for MONAI, which has been pre-trained on the MUG500+ skull dataset. The implementation follows the MONAI contribu…
k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala +5
Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Me…
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines
Jiachen Sun, Akshay Mehra, Bhavya Kailkhura +4
Certified robustness guarantee gauges a model's robustness to test-time attacks and can assess the model's readiness for deployment in the real world. In this work, we critically e…
Fully-automated Body Composition Analysis in Routine CT Imaging Using 3D Semantic Segmentation Convolutional Neural Networks
Sven Koitka, Lennard Kroll, Eugen Malamutmann +2
Body tissue composition is a long-known biomarker with high diagnostic and prognostic value in cardiovascular, oncological and orthopaedic diseases, but also in rehabilitation medi…