AIMC Topic: Algorithms

Clear Filters Showing 11471 to 11480 of 28713 articles

GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention.

IEEE transactions on pattern analysis and machine intelligence
Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention mo...

Detailed Avatar Recovery From Single Image.

IEEE transactions on pattern analysis and machine intelligence
This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the huma...

Learning to Forget for Meta-Learning via Task-and-Layer-Wise Attenuation.

IEEE transactions on pattern analysis and machine intelligence
Few-shot learning is an emerging yet challenging problem in which the goal is to achieve generalization from only few examples. Meta-learning tackles few-shot learning via the learning of prior knowledge shared across tasks and using it to learn new ...

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bound...

GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-Ray Images.

IEEE journal of biomedical and health informatics
Locating diseases in chest X-ray images with few careful annotations saves large human effort. Recent works approached this task with innovative weakly-supervised algorithms such as multi-instance learning (MIL) and class activation maps (CAM), howev...

A Fully Deep Learning Paradigm for Pneumoconiosis Staging on Chest Radiographs.

IEEE journal of biomedical and health informatics
Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learning has shown proven advantages in the detection of pneumoconiosis, it remains challenging in pneumoc...

Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.

IEEE journal of biomedical and health informatics
Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG...

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.

Frontiers in public health
OBJECTIVE: The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the ...

Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach.

Computational intelligence and neuroscience
Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a ...

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Computational and mathematical methods in medicine
RESULTS: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accur...