AIMC Topic: Algorithms

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Deep Learning Reconstruction Plus Single-Energy Metal Artifact Reduction for Supra Hyoid Neck CT in Patients With Dental Metals.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
We investigated the effect of deep learning reconstruction (DLR) plus single-energy metal artifact reduction (SEMAR) on neck CT in patients with dental metals, comparing it with DLR and with hybrid iterative reconstruction (Hybrid IR)-SEMAR. In thi...

Prediction of IDO1 Inhibitors by a Fingerprint-Based Stacking Ensemble Model Named IDO1Stack.

ChemMedChem
Indoleamine 2,3-dioxygenase 1 (IDO1) is viewed as an extremely promising target for cancer immunotherapy. Here, we proposed a two-layer stacking ensemble model, IDO1Stack, that can efficiently predict IDO1 inhibitors. First, we constructed a series o...

A novel framework of prescribed time/fixed time/finite time stochastic synchronization control of neural networks and its application in image encryption.

Neural networks : the official journal of the International Neural Network Society
In this paper, we investigate a novel framework for achieving prescribed-time (PAT), fixed-time (FXT) and finite-time (FNT) stochastic synchronization control of semi-Markov switching quaternion-valued neural networks (SMS-QVNNs), where the setting t...

Learning Rates for Nonconvex Pairwise Learning.

IEEE transactions on pattern analysis and machine intelligence
Pairwise learning is receiving increasing attention since it covers many important machine learning tasks, e.g., metric learning, AUC maximization, and ranking. Investigating the generalization behavior of pairwise learning is thus of great significa...

Unsupervised Learning of Graph Matching With Mixture of Modes via Discrepancy Minimization.

IEEE transactions on pattern analysis and machine intelligence
Graph matching (GM) has been a long-standing combinatorial problem due to its NP-hard nature. Recently (deep) learning-based approaches have shown their superiority over the traditional solvers while the methods are almost based on supervised learnin...

Normalization Techniques in Training DNNs: Methodology, Analysis and Application.

IEEE transactions on pattern analysis and machine intelligence
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications. This paper reviews and comments on the past, present and fu...

SequenceMorph: A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences.

IEEE transactions on pattern analysis and machine intelligence
Modern medical imaging techniques, such as ultrasound (US) and cardiac magnetic resonance (MR) imaging, have enabled the evaluation of myocardial deformation directly from an image sequence. While many traditional cardiac motion tracking methods have...

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-Local Spatial-Temporal Similarity.

IEEE transactions on pattern analysis and machine intelligence
We present compact and effective deep convolutional neural networks (CNNs) by exploring properties of videos for video deblurring. Motivated by the non-uniform blur property that not all the pixels of the frames are blurry, we develop a CNN to integr...

Learning Good Features to Transfer Across Tasks and Domains.

IEEE transactions on pattern analysis and machine intelligence
Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to solve different tasks share the same architecture suggests that there...

Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models.

IEEE transactions on pattern analysis and machine intelligence
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision trans...