Search Results for author:
Found 16 papers, 7 papers with code
Super-resolution of biomedical volumes with 2D supervision
Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human pathologists and computational pathology models.
Step-Calibrated Diffusion for Biomedical Optical Image Restoration
Here, we present Restorative Step-Calibrated Diffusion (RSCD), an unpaired image restoration method that views the image restoration problem as completing the finishing steps of a diffusion-based image generation task.
Cross-Resolution Land Cover Classification Using Outdated Products and Transformers
In this paper, we propose a Transformerbased weakly supervised method for cross-resolution land cover classification using outdated data.
A self-supervised framework for learning whole slide representations
Self-supervised representation learning can achieve high-quality WSI visual feature learning for downstream diagnostic tasks, such as cancer diagnosis or molecular genetic prediction.
Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters
SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists.
Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging
Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment.
Hierarchical discriminative learning improves visual representations of biomedical microscopy
Importantly, sampled patches from WSIs of a patient's tumor are a diverse set of image examples that capture the same underlying cancer diagnosis.
MixFormer: End-to-End Tracking with Iterative Mixed Attention
Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
Ranked #1 on
Visual Object Tracking
on TrackingNet
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology
Here, we present OpenSRH, the first public dataset of clinical SRH images from 300+ brain tumors patients and 1300+ unique whole slide optical images.
3D Shuffle-Mixer: An Efficient Context-Aware Vision Learner of Transformer-MLP Paradigm for Dense Prediction in Medical Volume
Therefore, designing an elegant and efficient vision transformer learner for dense prediction in medical volume is promising and challenging.
MixFormer: End-to-End Tracking with Iterative Mixed Attention
Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
Ranked #6 on
Visual Object Tracking
on UAV123
Semi-Supervised Video Object Segmentation
Visual Object Tracking
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing
Face anti-spoofing is an important task to protect the security of face recognition.
Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence
Conclusion: SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.
Target Transformed Regression for Accurate Tracking
Accurate tracking is still a challenging task due to appearance variations, pose and view changes, and geometric deformations of target in videos.
Ranked #1 on
Visual Object Tracking
on VOT2019
Blind deblurring for microscopic pathology images using deep learning networks
Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency.
Fully Convolutional Online Tracking
To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target filter based tracking paradigm.
