Search Results for author:
Found 8 papers, 6 papers with code
UniCell: Universal Cell Nucleus Classification via Prompt Learning
The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis.
Prompt-based Grouping Transformer for Nucleus Detection and Classification
Automatic nuclei detection and classification can produce effective information for disease diagnosis.
Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection
Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of histopathology.
Myopia prediction for adolescents via time-aware deep learning
Result: The mean absolute prediction error on the testing set was 0. 273-0. 257 for spherical equivalent, ranging from 0. 189-0. 160 to 0. 596-0. 473 if we consider different lengths of historical records and different prediction durations.
View-Disentangled Transformer for Brain Lesion Detection
Deep neural networks (DNNs) have been widely adopted in brain lesion detection and segmentation.
Attentive Symmetric Autoencoder for Brain MRI Segmentation
Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding.
Estimating Human Weight from A Single Image
In this case, the widely used Body Mass Index (BMI) which is associated with body height and weight can be employed as a measure of weight to indicate the health conditions.
Edge Prior Augmented Networks for Motion Deblurring on Naturally Blurry Images
Motion deblurring has witnessed rapid development in recent years, and most of the recent methods address it by using deep learning techniques, with the help of different kinds of prior knowledge.
