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Deep segmentation networks predict survival of non-small cell lung cancer.

Scientific reports
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/comp...

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data.

BMC medical informatics and decision making
BACKGROUND: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settin...

Deep learning segmentation of major vessels in X-ray coronary angiography.

Scientific reports
X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable tr...

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.

Scientific reports
Labeled medical imaging data is scarce and expensive to generate. To achieve generalizable deep learning models large amounts of data are needed. Standard data augmentation is a method to increase generalizability and is routinely performed. Generati...

Gene expression based survival prediction for cancer patients-A topic modeling approach.

PloS one
Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard ...

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures.

Medical image analysis
The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineati...

Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.

Medical image analysis
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we pro...

Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Medical image analysis
Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which results in little to no contrast at the lung boundary. For segmentation of severely injured lungs, loc...

Athena: Automated Tuning of k-mer based Genomic Error Correction Algorithms using Language Models.

Scientific reports
The performance of most error-correction (EC) algorithms that operate on genomics reads is dependent on the proper choice of its configuration parameters, such as the value of k in k-mer based techniques. In this work, we target the problem of findin...