AIMC Topic: Deep Learning

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Advanced SERSome-based artificial-intelligence technology for identifying medicinal and edible homologs.

Talanta
Medicinal and edible homologs (MEHs) offer significant preventive and therapeutic benefits for various diseases and health functions. However, the widespread application of MEHs faces significant challenges, particularly in quality control and rapid ...

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography.

Academic radiology
BACKGROUND: Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP).

Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.

Japanese journal of radiology
PURPOSE: Myocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality...

SSAT-Swin: Deep Learning-Based Spinal Ultrasound Feature Segmentation for Scoliosis Using Self-Supervised Swin Transformer.

Ultrasound in medicine & biology
OBJECTIVE: Scoliosis, a 3-D spinal deformity, requires early detection and intervention. Ultrasound curve angle (UCA) measurement using ultrasound images has emerged as a promising diagnostic tool. However, calculating the UCA directly from ultrasoun...

UniSAL: Unified Semi-supervised Active Learning for histopathological image classification.

Medical image analysis
Histopathological image classification using deep learning is crucial for accurate and efficient cancer diagnosis. However, annotating a large amount of histopathological images for training is costly and time-consuming, leading to a scarcity of avai...

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

World neurosurgery
BACKGROUND: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning a...

DCAlexNet: Deep coupled AlexNet for micro facial expression recognition based on double face images.

Computers in biology and medicine
Facial Micro-Expression Recognition (FER) presents challenges due to individual variations in emotional intensity and the complexity of feature extraction. While apex frames offer valuable emotional information, their precise role in FER remains uncl...

Progressive multi-task learning for fine-grained dental implant classification and segmentation in CBCT image.

Computers in biology and medicine
With the ongoing advancement of digital technology, oral medicine transitions from traditional diagnostics to computer-assisted diagnosis and treatment. Identifying dental implants in patients without records is complex and time-consuming. Accurate i...

Interpretable deep learning for deconvolutional analysis of neural signals.

Neuron
The widespread adoption of deep learning to model neural activity often relies on "black-box" approaches that lack an interpretable connection between neural activity and network parameters. Here, we propose using algorithm unrolling, a method for in...

Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning.

Biomacromolecules
We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification of large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). We used ∼6500 IDP s...