AIMC Topic: Deep Learning

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Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning.

Small methods
Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years....

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

Sensors (Basel, Switzerland)
The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT ...

A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays.

Critical care (London, England)
BACKGROUND: A quantitative assessment of pulmonary edema is important because the clinical severity can range from mild impairment to life threatening. A quantitative surrogate measure, although invasive, for pulmonary edema is the extravascular lung...

Evaluation of motion artefact reduction depending on the artefacts' directions in head MRI using conditional generative adversarial networks.

Scientific reports
Motion artefacts caused by the patient's body movements affect magnetic resonance imaging (MRI) accuracy. This study aimed to compare and evaluate the accuracy of motion artefacts correction using a conditional generative adversarial network (CGAN) w...

Decoding the physiological response of plants to stress using deep learning for forecasting crop loss due to abiotic, biotic, and climatic variables.

Scientific reports
This paper presents a simple method for detecting both biotic and abiotic stress in plants. Stress levels are measured based on the increase in nutrient uptake by plants as a mechanism of self-defense when under stress. A continuous electrical resist...

A divide and conquer approach to maximise deep learning mammography classification accuracies.

PloS one
Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However,...

Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Nature chemical biology
Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning ...

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

Journal of applied clinical medical physics
PURPOSE: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients.

Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.

The Journal of pathology
The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify...