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Found 27 papers, 12 papers with code
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
Although deep unsupervised domain adaptation (UDA) can leverage well-established source domain annotations and abundant target domain data to facilitate cross-modality image segmentation and also mitigate the label paucity problem on the target domain, the conventional UDA methods suffer from severe performance degradation when source domain annotations are scarce.
Adaptive Mean-Residue Loss for Robust Facial Age Estimation
Automated facial age estimation has diverse real-world applications in multimedia analysis, e. g., video surveillance, and human-computer interaction.
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements.
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Diabetic retinopathy (DR) is a common retinal disease that leads to blindness.
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images.
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks.
Deeply Supervised Active Learning for Finger Bones Segmentation
Segmentation is a prerequisite yet challenging task for medical image analysis.
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation
We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.
Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especially in clinical practice.
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation.
A Deep Framework for Bone Age Assessment based on Finger Joint Localization
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
Multi-Target Deep Learning for Algal Detection and Classification
Water quality has a direct impact on industry, agriculture, and public health.
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine.
Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide.
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
Diabetes is one of the most common disease in individuals.
Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading
Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients.
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring.
Automatic Liver And Tumor Segmentation
Image Segmentation
+3
Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood.
Residual Channel Attention Network for Brain Glioma Segmentation
A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality.
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection
Deep learning has achieved notable success in 3D object detection with the advent of large-scale point cloud datasets.
MetaGrad: Adaptive Gradient Quantization with Hypernetworks
A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference.
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
Our method demonstrates promising segmentation performance with a mean Dice score of 83. 8% and 81. 4% and an average asymmetric surface distance (ASSD) of 0. 55 mm and 0. 26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance.
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis.
Graph Neural Networks for Protein-Protein Interactions - A Short Survey
Protein-protein interactions (PPIs) play key roles in a broad range of biological processes.
