Tag: Multi-task learning

A single attention-gated multi-task network achieved classification and segmentation accuracy comparable to separate dedicated models on breast ultrasound images while ...

A dual-decoder deep-learning network localized 14 shoulder bony landmarks on contralateral CT scans with a mean radial error of 2.1 ...

UniLoopNet predicts enhancer–promoter, enhancer–enhancer, CTCF-mediated, and stripe-associated chromatin interactions from DNA sequence using a multi-task deep learning framework.

A unified deep learning framework called MyoSpectrumNet combines pathologic myopia screening, maculopathy grading, refraction estimation and lesion segmentation from heterogeneous ...

A dual-channel machine-learning framework combined sentiment, academic performance and engagement data to predict educational AI adoption, learning gains and personalized ...

FewShot AdaptiveMLC raised macro-F1 by 0.072 on a multi-label Bangla hate speech dataset by fine-tuning task-specific heads for minority labels ...

UAC-Net combined contextual attention and multi-scale feature fusion in a shared neural network that classified three brain-tumor types and delineated ...

Researchers at the Vellore Institute of Technology developed three hybrid deep learning models combining convolutional networks, transformers, and clinical metadata ...

A multi-task deep learning system reads infant hip ultrasounds, segments seven anatomical structures, localizes six landmarks, and measures Graf angles ...

Researchers have developed and validated an AI-driven adaptive learning system that predicts student engagement, forecasts academic achievement, and generates explainable ...

A new Genome Biology study introduces Pep2Net, a platform combining a knowledge base built from 52 peptide databases with a ...

Researchers have built a holographic neural network that uses a single spatial light modulator and one beam of light to ...

A new hybrid deep learning architecture combining a fine-tuned U-Net with a time-weighted multi-task bi-directional LSTM significantly improves real-time detection ...

A new knowledge distillation framework compresses three large neural networks into a 6.97 MB model that classifies rice diseases, localizes ...

Researchers have developed a hierarchical temporal graph network that predicts student academic outcomes with high accuracy while using gradient-based attribution ...

A multi-task deep learning model that fuses pretreatment MRI and whole slide pathology images outperformed standard staging in predicting chemotherapy ...

A new multi-task deep learning framework called ApexTransNet segments apical periodontitis lesions and grades radiographic severity on periapical radiographs with ...

A controlled head-to-head study shows Vision Transformers beat convolutional networks on real X-ray diffraction data once fine-tuned, while CNNs remain ...

Researchers at Keio University have developed H-PLE, a hierarchical multi-task recommendation framework that stabilizes expert routing and improves prediction of ...

A new multi-task AI framework called TAZD-Net segments thyroid nodules and predicts malignancy by explicitly reasoning over the lesion, its ...