AIMC Topic: Deep Learning

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Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.

Journal of the American Chemical Society
Fluorescence lifetime imaging microscopy (FLIM) is a powerful optical tool widely used in biomedical research to study changes in a sample's microenvironment. However, data collection and interpretation are often challenging, and traditional methods ...

Multitask Deep Learning Based on Longitudinal CT Images Facilitates Prediction of Lymph Node Metastasis and Survival in Chemotherapy-Treated Gastric Cancer.

Cancer research
UNLABELLED: Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer receiving neoadjuvant chemotherapy, providing timely guidance for clinical dec...

Differential dementia detection from multimodal brain images in a real-world dataset.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Artificial intelligence (AI) models have been applied to differential dementia detection tasks in brain images from curated, high-quality benchmark databases, but not real-world data in hospitals.

A novel deep learning system for automated diagnosis and grading of lumbar spinal stenosis based on spine MRI: model development and validation.

Neurosurgical focus
OBJECTIVE: The study aimed to develop a single-stage deep learning (DL) screening system for automated binary and multiclass grading of lumbar central stenosis (LCS), lateral recess stenosis (LRS), and lumbar foraminal stenosis (LFS).

Deep learning-based clinical decision support system for intracerebral hemorrhage: an imaging-based AI-driven framework for automated hematoma segmentation and trajectory planning.

Neurosurgical focus
OBJECTIVE: Intracerebral hemorrhage (ICH) remains a critical neurosurgical emergency with high mortality and long-term disability. Despite advancements in minimally invasive techniques, procedural precision remains limited by hematoma complexity and ...

Generation of synthetic CT-like imaging of the spine from biplanar radiographs: comparison of different deep learning architectures.

Neurosurgical focus
OBJECTIVE: This study compared two deep learning architectures-generative adversarial networks (GANs) and convolutional neural networks combined with implicit neural representations (CNN-INRs)-for generating synthetic CT (sCT) images of the spine fro...

Zero-shot segmentation of spinal vertebrae with metastatic lesions: an analysis of Meta's Segment Anything Model 2 and factors affecting learning free segmentation.

Neurosurgical focus
OBJECTIVE: Accurate vertebral segmentation is an important step in imaging analysis pipelines for diagnosis and subsequent treatment of spinal metastases. Segmenting these metastases is especially challenging given their radiological heterogeneity. C...

Image-based detection of the internal carotid arteries and sella turcica in endoscopic endonasal transsphenoidal surgery.

Neurosurgical focus
OBJECTIVE: Endoscopic endonasal transsphenoidal surgery (EETS) is a minimally invasive procedure that accesses the sellar and parasellar regions. Various anatomical structures must be identified during the operation, particularly the sella turcica an...

Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.

Journal of vision
Visual experience is known to play a critical role in face recognition. This experience is thought to enable the formation of a view-invariant representation by learning which features are critical to identify faces across views. Discovering these cr...

Equitable Deep Learning for Diabetic Retinopathy Detection Using Multidimensional Retinal Imaging With Fair Adaptive Scaling.

Translational vision science & technology
PURPOSE: To investigate the fairness of existing deep models for diabetic retinopathy (DR) detection and introduce an equitable model to reduce group performance disparities.