Despite the remarkable success of deep learning systems over the last de...
Data augmentation (DA) is a key factor in medical image analysis, such a...
Independently trained machine learning models tend to learn similar feat...
The accurate detection of suspicious regions in medical images is an
err...
Foundation models have taken over natural language processing and image
...
The medical imaging community generates a wealth of datasets, many of wh...
The accurate detection of mediastinal lesions is one of the rarely explo...
Minerals are indispensable for a functioning modern society. Yet, their
...
Unsupervised anomaly detection in medical imaging aims to detect and loc...
Artificial Intelligence (AI) is having a tremendous impact across most a...
In clinical radiology reports, doctors capture important information abo...
Semantic segmentation is one of the most popular research areas in medic...
The ability to estimate how a tumor might evolve in the future could hav...
Neural Processes (NPs) are a family of conditional generative models tha...
Simultaneous localisation and categorization of objects in medical image...
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. T...
Segmentation of endoscopic images is an essential processing step for
co...
Intraoperative tracking of laparoscopic instruments is often a prerequis...
Segmentation of abdominal organs has been a comprehensive, yet unresolve...
There is a large body of literature linking anatomic and geometric
chara...
Through training on unlabeled data, anomaly detection has the potential ...
Recent advances in artificial intelligence research have led to a profus...
The U-Net is arguably the most successful segmentation architecture in t...
Existing approaches to modeling the dynamics of brain tumor growth,
spec...
Medical imaging only indirectly measures the molecular identity of the t...
Fueled by the diversity of datasets, semantic segmentation is a popular
...
While the major white matter tracts are of great interest to numerous st...
Unsupervised learning can leverage large-scale data sources without the ...
The task of localizing and categorizing objects in medical images often
...
Gliomas are the most common primary brain malignancies, with different
d...
The U-Net was presented in 2015. With its straight-forward and successfu...
In this paper we demonstrate the effectiveness of a well trained U-Net i...
End-to-end deep learning improves breast cancer classification on
diffus...
While the major white matter tracts are of great interest to numerous st...
Many real-world vision problems suffer from inherent ambiguities. In cli...
The individual course of white matter fiber tracts is an important key f...
Bone segmentation from CT images is a task that has been worked on for
d...
Quantitative analysis of brain tumors is critical for clinical decision
...
Cardiac magnetic resonance imaging improves on diagnosis of cardiovascul...
The state-of-the-art method for automatically segmenting white matter bu...
Mammography screening for early detection of breast lesions currently su...