AIMC Topic: Humans

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Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach.

Talanta
This work presents a robust strategy for quantifying overlapping electrochemical signatures originating from complex mixtures and real human plasma samples using nickel-based electrochemical sensors and machine learning (ML). This strategy enables th...

DICCR: Double-gated intervention and confounder causal reasoning for vision-language navigation.

Neural networks : the official journal of the International Neural Network Society
Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text ins...

Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.

Medical image analysis
Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular su...

Synth-CLIP: Synthetic data make CLIP generalize better in data-limited scenarios.

Neural networks : the official journal of the International Neural Network Society
Prompt learning is a powerful technique that enables the transfer of Vision-Language Models (VLMs) like CLIP to downstream tasks. However, when the prompt-based methods are fine-tuned solely on base classes, they often struggle to generalize to novel...

Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.

Medical image analysis
Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on hi...

Multi-scale multi-object semi-supervised consistency learning for ultrasound image segmentation.

Neural networks : the official journal of the International Neural Network Society
Manual annotation of ultrasound images relies on expert knowledge and requires significant time and financial resources. Semi-supervised learning (SSL) exploits large amounts of unlabeled data to improve model performance under limited labeled data. ...

MPIC: Exploring alternative approach to standard convolution in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explore...

DFedGFM: Pursuing global consistency for Decentralized Federated Learning via global flatness and global momentum.

Neural networks : the official journal of the International Neural Network Society
To tackle high communication costs and privacy issues in Centralized Federated Learning (CFL), Decentralized Federated Learning (DFL) is an alternative. However, a significant discrepancy exists between local updates and the expected global update, k...

Fast ramp fraction loss SVM classifier with low computational complexity for pattern classification.

Neural networks : the official journal of the International Neural Network Society
The support vector machine (SVM) is a powerful tool for pattern classification thanks to its outstanding efficiency. However, when encountering extensive classification tasks, the considerable computational complexity may present a substantial barrie...

Machine Learning Prediction of Early Recurrence in Gastric Cancer: A Nationwide Real-World Study.

Annals of surgical oncology
BACKGROUND: Patients with gastric cancer (GC) who experience early recurrence (ER) within 2 years postoperatively have poor prognoses. This study aimed to analyze and predict ER after curative surgery for patients with GC using machine learning (ML) ...