AIMC Topic: Algorithms

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Attributes learning network for generalized zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
In the absence of unseen training data, zero-shot learning algorithms utilize the semantic knowledge shared by the seen and unseen classes to establish the connection between the visual space and the semantic space, so as to realize the recognition o...

Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) - Changing the Way We Validate Classification Algorithms.

Journal of medical systems
Adoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability of th...

Challenges in translational machine learning.

Human genetics
Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as "translational machine learning", joint efforts and strong communication between data scientists and cli...

Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction.

The British journal of radiology
OBJECTIVE: To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstructed with deep learning image reconstruction (DLIR).

Deep learning improves image quality and radiomics reproducibility for high-speed four-dimensional computed tomography reconstruction.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Hybrid iterative reconstruction (HIR) is the most commonly used algorithm for four-dimensional computed tomography (4DCT) reconstruction due to its high speed. However, the image quality is worse than that of model-based itera...

Artificial Intelligence and Mechanical Circulatory Support.

Heart failure clinics
Advances in machine learning algorithms and computing power have fueled a rapid increase in artificial intelligence research in health care, including mechanical circulatory support. In this review, we highlight the needs for artificial intelligence ...

Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet.

IEEE transactions on pattern analysis and machine intelligence
Adversarial attacks on deep neural networks (DNNs) have been found for several years. However, the existing adversarial attacks have high success rates only when the information of the victim DNN is well-known or could be estimated by the structure s...

A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation.

IEEE transactions on pattern analysis and machine intelligence
Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most wide...

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization.

IEEE transactions on pattern analysis and machine intelligence
Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantages of human ingenuity and prior knowledge. Thus it h...

A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning.

IEEE transactions on pattern analysis and machine intelligence
Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that deep models are vulnerable to adversarial examples, which are crafted by...