AIMC Topic: Image Processing, Computer-Assisted

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Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty.

Physics in medicine and biology
There can be significant uncertainty when identifying cervical lymph node (LN) metastases in patients with oropharyngeal squamous cell carcinoma (OPSCC) despite the use of modern imaging modalities such as positron emission tomography (PET) and compu...

Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

PloS one
Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapt...

Artificial Intelligence for Response Evaluation With PET/CT.

Seminars in nuclear medicine
Positron emission tomography (PET)/computed tomography (CT) are nuclear diagnostic imaging modalities that are routinely deployed for cancer staging and monitoring. They hold the advantage of detecting disease related biochemical and physiologic abno...

Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images.

Seminars in nuclear medicine
Artificial intelligence (AI) has recently attracted much attention for its potential use in healthcare applications. The use of AI to improve and extract more information out of medical images, given their parallels with natural images and the immens...

Deeply-supervised density regression for automatic cell counting in microscopy images.

Medical image analysis
Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains cha...

rest2vec: Vectorizing the resting-state functional connectome using graph embedding.

NeuroImage
Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used in connectomics for studying the functional relationships between regions of the human brain. rs-fMRI connectomics, however, has inherent analytical challenges, such as how ...

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

Scientific reports
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the ...

CT-ORG, a new dataset for multiple organ segmentation in computed tomography.

Scientific data
Despite the relative ease of locating organs in the human body, automated organ segmentation has been hindered by the scarcity of labeled training data. Due to the tedium of labeling organ boundaries, most datasets are limited to either a small numbe...

Progressive multifocal leukoencephalopathy lesion and brain parenchymal segmentation from MRI using serial deep convolutional neural networks.

NeuroImage. Clinical
Progressive multifocal leukoencephalopathy (PML) is a rare opportunistic brain infection caused by the JC virus and associated with substantial morbidity and mortality. Accurate MRI assessment of PML lesion burden and brain parenchymal atrophy is of ...

Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet.

EBioMedicine
BACKGROUND: Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is al...