AIMC Topic: Deep Learning

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MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism.

Journal of chemical information and modeling
Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they ...

Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years.

Journal of chemical information and modeling
Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and environmental science. Over the last decades, diverse computational techniques have been developed, from using simple physical and chemical properties and molecular...

Rapid Identification of Drug Mechanisms with Deep Learning-Based Multichannel Surface-Enhanced Raman Spectroscopy.

ACS sensors
Rapid identification of drug mechanisms is vital to the development and effective use of chemotherapeutics. Herein, we develop a multichannel surface-enhanced Raman scattering (SERS) sensor array and apply deep learning approaches to realize the rapi...

Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Toxins
Fusarium head blight (FHB) is a plant disease caused by various species of the fungus. One of the major concerns associated with spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and anim...

Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images.

Tomography (Ann Arbor, Mich.)
Anterior cruciate ligament (ACL) tears are prevalent knee injures, particularly among active individuals. Accurate and timely diagnosis is essential for determining the optimal treatment strategy and assessing patient prognosis. Various previous stud...

Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.

Scientific reports
This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model's transferabilit...

End-to-end reproducible AI pipelines in radiology using the cloud.

Nature communications
Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Al...

Real-world evaluation of RetCAD deep-learning system for the detection of referable diabetic retinopathy and age-related macular degeneration.

Clinical & experimental optometry
CLINICAL RELEVANCE: The challenges of establishing retinal screening programs in rural settings may be mitigated by the emergence of deep-learning systems for early disease detection.

Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Fully automatic analysis of myocardial perfusion cardiovascular magnetic resonance imaging datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning t...

Automated Cerebrovascular Segmentation and Visualization of Intracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learning.

Journal of imaging informatics in medicine
Time-of-flight magnetic resonance angiography (TOF-MRA) is a non-contrast technique used to visualize neurovascular. However, manual reconstruction of the volume render (VR) by radiologists is time-consuming and labor-intensive. Deep learning-based (...