Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …
segmentation models based on convolutional neural networks. Despite the new …
Advances in human intracranial electroencephalography research, guidelines and good practices
Since the second half of the twentieth century, intracranial electroencephalography (iEEG),
including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG) …
including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG) …
Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency
Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …
performance in many medical image segmentation tasks, they rely on a large set of labeled …
Inf-net: Automatic covid-19 lung infection segmentation from ct images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to
face an existential health crisis. Automated detection of lung infections from computed …
face an existential health crisis. Automated detection of lung infections from computed …
Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation
Training deep convolutional neural networks usually requires a large amount of labeled
data. However, it is expensive and time-consuming to annotate data for medical image …
data. However, it is expensive and time-consuming to annotate data for medical image …
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using …
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using …
Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency
Abstract Gross Target Volume (GTV) segmentation plays an irreplaceable role in
radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional …
radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional …
Weakly supervised segmentation of COVID19 infection with scribble annotation on CT images
Segmentation of infections from CT scans is important for accurate diagnosis and follow-up
in tackling the COVID-19. Although the convolutional neural network has great potential to …
in tackling the COVID-19. Although the convolutional neural network has great potential to …
Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation
Semi-supervised learning has greatly advanced medical image segmentation since it
effectively alleviates the need of acquiring abundant annotations from experts, wherein the …
effectively alleviates the need of acquiring abundant annotations from experts, wherein the …
Xnet: Wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images
Fully-and semi-supervised semantic segmentation of biomedical images have been
advanced with the development of deep neural networks (DNNs). So far, however, DNN …
advanced with the development of deep neural networks (DNNs). So far, however, DNN …