AIMC Topic: Deep Learning

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Genomic prediction of feed efficiency in boars by deep learning.

G3 (Bethesda, Md.)
Pork is the most widely consumed meat globally, and the industry has achieved substantial genetic advancements for several traits using genomic selection. However, traditional linear genomic prediction models may be inadequate for predicting complex ...

Dimensionality reduction of genetic data using contrastive learning.

Genetics
We introduce a framework for using contrastive learning for dimensionality reduction on genetic datasets to create principal component analysis (PCA)-like population visualizations. Contrastive learning is a self-supervised deep learning method that ...

[Artificial intelligence empowering sports medicine].

Zhonghua yi xue za zhi
The rapid advancement of artificial intelligence (AI) technologies, particularly deep learning algorithms and hardware devices, has profoundly transformed diagnostic and therapeutic paradigms in sports medicine. This article reviews the applications ...

An Integrated Deep Learning and Large Language Model for Burn Wound Depth Recognition.

Journal of burn care & research : official publication of the American Burn Association
Accurate burn depth assessment remains a challenge, especially in emergency settings. This study aimed to develop a low-cost artificial intelligence (AI)-based system for burn wound classification using deep learning and large language models (LLMs)....

[Review of application of U-Net and Transformer in colon polyp image segmentation].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Colorectal cancer typically originates from the malignant transformation of colonic polyps, making the automatic and accurate segmentation of colonic polyps crucial for clinical diagnosis. Deep learning techniques such as U-Net and Transformer can ef...

[Research progress on quantitative magnetic susceptibility imaging reconstruction method based on improved U-network model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Quantitative magnetic susceptibility imaging (QSM) is an imaging method based on magnetic resonance imaging (MRI) phase signal processing and inversion to obtain tissue magnetic susceptibility distribution, which can generate images reflecting the ma...

[Image classification of osteoarthritis based on improved shifted windows transformer and graph convolutional networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Osteoarthritis is a common degenerative joint disease, which is often analyzed by X-ray images. However, if there is a lack of clinical experience when reading the films, it is easy to cause misdiagnosis. Although deep learning has made significant p...

[Adaptive lesion-aware fusion network for joint grading of multiple fundus diseases].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Diabetic retinopathy (DR) and its complication, diabetic macular edema (DME), are major causes of visual impairment and even blindness. The occurrence of DR and DME is pathologically interconnected, and their clinical diagnoses are closely related. J...

[Automatic detection and visualization of myocardial infarction in electrocardiograms based on an interpretable deep learning model].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Automated detection of myocardial infarction (MI) is crucial for preventing sudden cardiac death and enabling early intervention in cardiovascular diseases. This paper proposes a deep learning framework based on a lightweight convolutional neural net...

Artifact-robust Deep Learning-based Segmentation of 3D Phase-contrast MR Angiography: A Novel Data Augmentation Approach.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
This study presents a novel data augmentation approach to improve deep learning (DL)-based segmentation for 3D phase-contrast magnetic resonance angiography (PC-MRA) images affected by pulsation artifacts. Augmentation was achieved by simulating puls...