AIMC Topic: Deep Learning

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Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement o...

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Ne...

Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.

Mycoses
BACKGROUND: Currently, the diagnosis of invasive pulmonary aspergillosis (IPA) mainly depends on the integration of clinical, radiological and microbiological data. Artificial intelligence (AI) has shown great advantages in dealing with data-rich bio...

Identification of Bacterial Pathogens at Genus and Species Levels through Combination of Raman Spectrometry and Deep-Learning Algorithms.

Microbiology spectrum
The rapid and accurate identification of the causing agents during bacterial infections would greatly improve pathogen transmission, prevention, patient care, and medical treatments in clinical settings. Although many conventional and molecular metho...

Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks.

Journal of chemical information and modeling
In this study, a framework for the prediction of thermophysical properties based on transfer learning from existing estimation models is explored. The predictive capabilities of conventional group-contribution methods and traditional machine-learning...

Incorporating the image formation process into deep learning improves network performance.

Nature methods
We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, es...

Automated detection of patterned single-cells within hydrogel using deep learning.

Scientific reports
Single-cell analysis has been widely used in various biomedical engineering applications, ranging from cancer diagnostics, and immune response monitoring to drug screening. Single-cell isolation is fundamental for observing single-cell activities and...

G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.

PLoS computational biology
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant ...

SkiNet: A deep learning framework for skin lesion diagnosis with uncertainty estimation and explainability.

PloS one
Skin cancer is considered to be the most common human malignancy. Around 5 million new cases of skin cancer are recorded in the United States annually. Early identification and evaluation of skin lesions are of great clinical significance, but the di...

Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese.

PloS one
Deep learning, in recent times, has made remarkable strides when it comes to impressive performance for many tasks, including medical image processing. One of the contributing factors to these advancements is the emergence of large medical image data...