Search Results for author:
Found 6 papers, 5 papers with code
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
Ranked #1 on
Organ Segmentation
on BTCV
LUMix: Improving Mixup by Better Modelling Label Uncertainty
LUMix is simple as it can be implemented in just a few lines of code and can be universally applied to any deep networks \eg CNNs and Vision Transformers, with minimal computational cost.
Synthetic Tumors Make AI Segment Tumors Better
We develop a novel strategy to generate synthetic tumors.
PartImageNet: A Large, High-Quality Dataset of Parts
To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations.
TransMix: Attend to Mix for Vision Transformers
The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map.
TransFG: A Transformer Architecture for Fine-grained Recognition
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences.
Ranked #4 on
Fine-Grained Image Classification
on CUB-200-2011
