Interactive few-shot learning: Limited supervision, better medical image segmentation
Many known supervised deep learning methods for medical image segmentation suffer an
expensive burden of data annotation for model training. Recently, few-shot segmentation …
expensive burden of data annotation for model training. Recently, few-shot segmentation …
Aircraft fuselage corrosion detection using artificial intelligence
Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued
structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed …
structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed …
Domain adaptation for medical image segmentation: a meta-learning method
P Zhang, J Li, Y Wang, J Pan - Journal of Imaging, 2021 - mdpi.com
Convolutional neural networks (CNNs) have demonstrated great achievement in increasing
the accuracy and stability of medical image segmentation. However, existing CNNs are …
the accuracy and stability of medical image segmentation. However, existing CNNs are …
Conditional generation of medical images via disentangled adversarial inference
Synthetic medical image generation has a huge potential for improving healthcare through
many applications, from data augmentation for training machine learning systems to …
many applications, from data augmentation for training machine learning systems to …
Unsupervised representation learning meets pseudo-label supervised self-distillation: A new approach to rare disease classification
Rare diseases are characterized by low prevalence and are often chronically debilitating or
life-threatening. Imaging-based classification of rare diseases is challenging due to the …
life-threatening. Imaging-based classification of rare diseases is challenging due to the …
Hybrid unsupervised representation learning and pseudo-label supervised self-distillation for rare disease imaging phenotype classification with dispersion-aware …
Rare diseases are characterized by low prevalence and are often chronically debilitating or
life-threatening. Imaging phenotype classification of rare diseases is challenging due to the …
life-threatening. Imaging phenotype classification of rare diseases is challenging due to the …
Adaptive Input-image Normalization for Solving Mode Collapse Problem in GAN-based X-ray Images
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases.
Generative Adversarial Networks play a key role in addressing this imbalance by enabling …
Generative Adversarial Networks play a key role in addressing this imbalance by enabling …
Personalized acute stress classification from physiological signals with neural processes
CL Stewart, A Folarin, R Dobson - arXiv preprint arXiv:2002.04176, 2020 - arxiv.org
Objective: A person's affective state has known relationships to physiological processes
which can be measured by wearable sensors. However, while there are general trends …
which can be measured by wearable sensors. However, while there are general trends …
Rare disease classification via difficulty-aware meta learning
Deep convolutional neural networks (ConvNets) have achieved state-of-the-art performance
in various medical image analysis tasks. The success is partially attributed to a large amount …
in various medical image analysis tasks. The success is partially attributed to a large amount …
Blend & Predict: Domain-Adaptable Few-Shot Learning for Microscopy Imaging
Accurate classification of microscopy images is critical for the analysis of biological samples.
The availability of large-scale labeled datasets has contributed to recent progress in training …
The availability of large-scale labeled datasets has contributed to recent progress in training …