AIMC Topic: Deep Learning

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Contrastive Registration for Unsupervised Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using deep learning supervised techniques, they assume a large and...

DSTCNet: Deep Spectro-Temporal-Channel Attention Network for Speech Emotion Recognition.

IEEE transactions on neural networks and learning systems
Speech emotion recognition (SER) plays an important role in human-computer interaction, which can provide better interactivity to enhance user experiences. Existing approaches tend to directly apply deep learning networks to distinguish emotions. Amo...

Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images.

IEEE transactions on neural networks and learning systems
Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of ...

Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment.

Frontiers in public health
BACKGROUND: Machine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. Existing models often assess PICC-RVT risk as static and di...

Empirical analysis on retinal segmentation using PSO-based thresholding in diabetic retinopathy grading.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapid growth of deep learning eases the clinicians' DR diagnosing procedure. It automatically extracts t...

DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to multiple unseen datasets. This is important for the clinical applications of DL-based models to breast...

ICH-PRNet: a cross-modal intracerebral haemorrhage prognostic prediction method using joint-attention interaction mechanism.

Neural networks : the official journal of the International Neural Network Society
Accurately predicting intracerebral hemorrhage (ICH) prognosis is a critical and indispensable step in the clinical management of patients post-ICH. Recently, integrating artificial intelligence, particularly deep learning, has significantly enhanced...

DFCL: Dual-pathway fusion contrastive learning for blind single-image visible watermark removal.

Neural networks : the official journal of the International Neural Network Society
Digital image watermarking is a prevalent method for image copyright protection. As watermark embedding techniques evolve, research in copyright protection has increasingly extended into watermark removal. Recent advancements in deep learning and gen...

Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning.

Radiological physics and technology
Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within image...

Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive intravascular optical coherence tomography analysis.

Physical and engineering sciences in medicine
Neointimal coverage and stent apposition, as assessed from intravascular optical coherence tomography (IVOCT) images, are crucial for optimizing percutaneous coronary intervention (PCI). Existing state-of-the-art computer algorithms designed to autom...