AIMC Topic: Deep Learning

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Rate of brain aging associates with future executive function in Asian children and older adults.

eLife
Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore...

Fast and accurate lung cancer subtype classication and localization based on Intraoperative frozen sections of lung adenocarcinoma.

Biomedical physics & engineering express
Current lung cancer diagnostic techniques primarily focus on tissue subtype classification, yet remain inadequate in distinguishing pathological progression subtypes (particularly between adenocarcinomaand invasive adenocarcinoma) on frozen sections....

MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.

Physics in medicine and biology
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...

Foster noisy label learning by exploiting noise-induced distortion in foreground localization.

Neural networks : the official journal of the International Neural Network Society
Large-scale, well-annotated datasets are crucial for training deep neural networks. However, the prevalence of noisy-labeled samples can cause irreversible impairment to the generalization of models. Existing approaches have attempted to mitigate the...

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

British journal of hospital medicine (London, England : 2005)
Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result in high false-positive rates, leading to unnecessary biopsies. Artificial intelligence (AI) has dem...

FFLUNet: Feature Fused Lightweight UNet for brain tumor segmentation.

Computers in biology and medicine
Brain tumors, particularly glioblastoma multiforme, are considered one of the most threatening types of tumors in neuro-oncology. Segmenting brain tumors is a crucial part of medical imaging. It plays a key role in diagnosing conditions, planning tre...

FDTooth: Intraoral Photographs and CBCT Images for Fenestration and Dehiscence Detection.

Scientific data
Fenestration and dehiscence (FD) pose significant challenges in dental treatments as they adversely affect oral health. Although cone-beam computed tomography (CBCT) provides precise diagnostics, its extensive time requirements and radiation exposure...

Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach.

European radiology experimental
OBJECTIVES: To evaluate a deep learning sequence-adaptive liver multiparametric MRI (mpMRI) assessment with validation in different populations using total and segmental T1 and T2 relaxation time maps.

Automated analysis of mouse rearing using deep learning.

Journal of pharmacological sciences
Rodent rearing behavior is frequently assessed as an indicator of anxiety and exploratory tendencies. This study developed a convolutional recurrent neural network (CRNN) model to detect mouse rearing using overhead videos. Behavioral data from C57BL...

Predicting drug-drug interactions: A deep learning approach with GCN-based collaborative filtering.

Artificial intelligence in medicine
The use of combination drugs among patients is increasing due to effectiveness compared to monotherapies. However, healthcare providers should continue to be concerned about the potential risks associated with patient safety arising from drug-drug in...