Scpnet: Semantic scene completion on point cloud
Training deep models for semantic scene completion is challenging due to the sparse and
incomplete input, a large quantity of objects of diverse scales as well as the inherent label …
incomplete input, a large quantity of objects of diverse scales as well as the inherent label …
An efficient deep learning-based skin cancer classifier for an imbalanced dataset
Efficient skin cancer detection using images is a challenging task in the healthcare domain.
In today's medical practices, skin cancer detection is a time-consuming procedure that may …
In today's medical practices, skin cancer detection is a time-consuming procedure that may …
Towards efficient 3d object detection with knowledge distillation
Despite substantial progress in 3D object detection, advanced 3D detectors often suffer from
heavy computation overheads. To this end, we explore the potential of knowledge distillation …
heavy computation overheads. To this end, we explore the potential of knowledge distillation …
Dissecting self-supervised learning methods for surgical computer vision
The field of surgical computer vision has undergone considerable breakthroughs in recent
years with the rising popularity of deep neural network-based methods. However, standard …
years with the rising popularity of deep neural network-based methods. However, standard …
Cross-resolution distillation for efficient 3D medical image registration
Images captured in clinic such as MRI scans are usually in 3D formats with high spatial
resolutions. Existing learning-based models for medical image registration consume large …
resolutions. Existing learning-based models for medical image registration consume large …
Self-supervised dual-head attentional bootstrap learning network for prostate cancer screening in transrectal ultrasound images
X Lu, X Liu, Z Xiao, S Zhang, J Huang, C Yang… - Computers in Biology …, 2023 - Elsevier
Current convolutional neural network-based ultrasound automatic classification models for
prostate cancer often rely on extensive manual labeling. Although Self-supervised Learning …
prostate cancer often rely on extensive manual labeling. Although Self-supervised Learning …
Discrepancy and gradient-guided multi-modal knowledge distillation for pathological glioma grading
The fusion of multi-modal data, eg, pathology slides and genomic profiles, can provide
complementary information and benefit glioma grading. However, genomic profiles are …
complementary information and benefit glioma grading. However, genomic profiles are …
Self-distilled hierarchical network for unsupervised deformable image registration
Unsupervised deformable image registration benefits from progressive network structures
such as Pyramid and Cascade. However, existing progressive networks only consider the …
such as Pyramid and Cascade. However, existing progressive networks only consider the …
Gradient modulated contrastive distillation of low-rank multi-modal knowledge for disease diagnosis
The fusion of multi-modal data, eg, medical images and genomic profiles, can provide
complementary information and further benefit disease diagnosis. However, multi-modal …
complementary information and further benefit disease diagnosis. However, multi-modal …
Contrastive functional connectivity graph learning for population-based fMRI classification
Contrastive self-supervised learning has recently benefited fMRI classification with inductive
biases. Its weak label reliance prevents overfitting on small medical datasets and tackles the …
biases. Its weak label reliance prevents overfitting on small medical datasets and tackles the …