AIMC Topic: Deep Learning

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Predicting time to live birth with deep learning embryo ranking: a novel multiple imputation approach.

Human reproduction (Oxford, England)
STUDY QUESTION: What is the clinical utility of embryo selection algorithms in estimating the time to live birth (TTLB)?

Multimodal deep learning for enhanced breast cancer diagnosis on sonography.

Computers in biology and medicine
This study introduces a novel multimodal deep learning model tailored for the differentiation of benign and malignant breast masses using dual-view breast ultrasound images (radial and anti-radial views) in conjunction with corresponding radiology re...

Accelerating Diffusion: Task-Optimized latent diffusion models for rapid CT denoising.

Computers in biology and medicine
Computed tomography (CT) systems are indispensable for diagnostics but pose risks due to radiation exposure. Low-dose CT (LDCT) mitigates these risks but introduces noise and artifacts that compromise diagnostic accuracy. While deep learning methods,...

Continual learning across population cohorts with distribution shift: insights from multi-cohort metabolic syndrome identification.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-h...

Selection, visualization, and explanation of deep features from resting-state fMRI for Alzheimer's disease diagnosis.

Psychiatry research. Neuroimaging
Despite the remarkable achievements of deep learning networks in analyzing neuroimaging data for various tasks linked to brain functions and disorders, the opaque nature of these models and their interpretability challenges pose significant barriers ...

Deep learning reconstruction for T2-weighted and contrast-enhanced T1-weighted magnetic resonance enterography imaging in patients with Crohn's disease: Assessment of image quality and clinical utility.

Clinical imaging
PURPOSE: To investigate the image quality of deep learning-reconstructed T2-weighted half-Fourier single-shot turbo spin echo (DL T2 HASTE) and contrast-enhanced T1-weighted volumetric interpolated breath-hold examination (DL T1 VIBE) of magnetic res...

Ensemble learning approach for detecting breast invasive ductal carcinoma from histopathological images.

Pathology, research and practice
Invasive ductal carcinoma is a type of breast cancer that is one of the most frequent and aggressive forms of breast malignancy, necessitating accurate and timely diagnosis for effective treatment. Though considered the gold standard, traditional his...

LiDSCUNet++: A lightweight depth separable convolutional UNet++ for vertebral column segmentation and spondylosis detection.

Research in veterinary science
Accurate computer-aided diagnosis systems rely on precise segmentation of the vertebral column to assist physicians in diagnosing various disorders. However, segmenting spinal disks and bones becomes challenging in the presence of abnormalities and c...

Artificial intelligence (AI)-driven morphological assessment of zebrafish larvae for developmental toxicity chemical screening.

Aquatic toxicology (Amsterdam, Netherlands)
Screening chemicals using the zebrafish embryo developmental toxicity assay requires visual assessment of larval morphological changes based on images by experienced screeners. The process is time-consuming and prone to subjectivity. However, deep le...

MSFusion: A multi-source hybrid feature fusion network for accurate grading of invasive breast cancer using H&E-stained histopathological images.

Medical image analysis
Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enhancing survival rates. However, accurately grading IBC presents a significant challenge due to its h...