AIMC Topic: Algorithms

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Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images.

IEEE journal of biomedical and health informatics
In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel...

FDDSeg: Unleashing the Power of Scribble Annotation for Cardiac MRI Images Through Feature Decomposition Distillation.

IEEE journal of biomedical and health informatics
Cardiovascular diseases can be diagnosed with computer assistance when using the magnetic resonance imaging (MRI) image that is produced by the MRI sensor. Deep learning-based scribbling MRI image segmentation has demonstrated impressive results rece...

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

IEEE journal of biomedical and health informatics
Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination predictio...

Dynamic Subcluster-Aware Network for Few-Shot Skin Disease Classification.

IEEE transactions on neural networks and learning systems
This article addresses the problem of few-shot skin disease classification by introducing a novel approach called the subcluster-aware network (SCAN) that enhances accuracy in diagnosing rare skin diseases. The key insight motivating the design of SC...

Adaptive Gait Feature Learning Using Mixed Gait Sequence.

IEEE transactions on neural networks and learning systems
Gait recognition has become a mainstream technology for identification, as it can recognize the identity of subjects from a distance without any cooperation. However, when subjects wear coats (CL) or backpacks (BG), their gait silhouette will be occl...

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...

SemiHAR: Improving Semisupervised Human Activity Recognition via Multitask Learning.

IEEE transactions on neural networks and learning systems
Semisupervised human activity recognition (SemiHAR) has attracted attention in recent years from various domains, such as digital health and ambient intelligence. Currently, it still faces two challenges. For one thing, discriminative features may ex...

A Semantic-Aware Attention and Visual Shielding Network for Cloth-Changing Person Re-Identification.

IEEE transactions on neural networks and learning systems
Cloth-changing person re-identification (ReID) is a newly emerging research topic that aims to retrieve pedestrians whose clothes are changed. Since the human appearance with different clothes exhibits large variations, it is very difficult for exist...

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...

Seeking a Hierarchical Prototype for Multimodal Gesture Recognition.

IEEE transactions on neural networks and learning systems
Gesture recognition has drawn considerable attention from many researchers owing to its wide range of applications. Although significant progress has been made in this field, previous works always focus on how to distinguish between different gesture...