AIMC Topic: Algorithms

Clear Filters Showing 2271 to 2280 of 28713 articles

Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey.

Methods (San Diego, Calif.)
Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to its complex biological mechanisms. Early and accurate diagnosis plays a fundamental role in improving...

Intelligent Detection and Recognition of Marine Plankton by Digital Holography and Deep Learning.

Sensors (Basel, Switzerland)
The detection, observation, recognition, and statistics of marine plankton are the basis of marine ecological research. In recent years, digital holography has been widely applied to plankton detection and recognition. However, the recording and reco...

A comprehensive review and evaluation of machine learning-based approaches for identifying tumor T cell antigens.

Computational biology and chemistry
The precise identification of tumor T-cell antigens (TTCAs) is crucial for advancements in cancer immunotherapy and other clinical uses. In contrast to the labor-intensive and time-consuming process of experimentally identifying TTCAs, computational ...

Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.

Abdominal radiology (New York)
One of the most prevalent cancers in the world is kidney cancer (KC). A precise diagnosis, which is influenced by a number of variables, such as the size or volume of the tumor, the types and stages of the cancer, etc., is essential for the treatment...

A fractional-order multi-delayed bicyclic crossed neural network: Stability, bifurcation, and numerical solution.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a fractional-order bicyclic crossed neural network (NN) with multiple time delays consisting of two sharing neurons between rings. The given fractional-order NN is defined in terms of the Caputo fractional derivatives. We pr...

GDVIFNet: A generated depth and visible image fusion network with edge feature guidance for salient object detection.

Neural networks : the official journal of the International Neural Network Society
In recent years, despite significant advancements in salient object detection (SOD), performance in complex interference environments remains suboptimal. To address these challenges, additional modalities like depth (SOD-D) or thermal imaging (SOD-T)...

Exploration and exploitation in continual learning.

Neural networks : the official journal of the International Neural Network Society
Continual learning (CL) has received a surge of interest, particularly in parameter isolation approaches, aiming to prevent catastrophic forgetting by assigning a disjoint parameter set to each task. Despite their effectiveness, existing approaches o...

A deep learning model for multiclass tooth segmentation on cone-beam computed tomography scans.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: Machine learning, a common artificial intelligence technology in medical image analysis, enables computers to learn statistical patterns from pairs of data and annotated labels. Supervised learning in machine learning allows the compute...

Open-source deep-learning models for segmentation of normal structures for prostatic and gynecological high-dose-rate brachytherapy: Comparison of architectures.

Journal of applied clinical medical physics
BACKGROUND: The use of deep learning-based auto-contouring algorithms in various treatment planning services is increasingly common. There is a notable deficit of commercially or publicly available models trained on large or diverse datasets containi...

Unsupervised brain MRI tumour segmentation via two-stage image synthesis.

Medical image analysis
Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learning offer an alternative by using synthetic data for training. However, the discrepancy between real...