research
∙
04/22/2023
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
The Segment Anything Model (SAM) is a recently developed large model for...
share
research
∙
11/15/2022
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning
High annotation costs and limited labels for dense 3D medical imaging ta...
research
∙
07/01/2022
Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models
We aim to quantitatively measure the practical usability of medical imag...
research
∙
06/02/2022
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
Deep learning (DL) based semantic segmentation methods have achieved exc...
research
∙
02/28/2019
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation
Supervised training a deep neural network aims to "teach" the network to...
research
∙
01/15/2019
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
The Encoder-Decoder architecture is a main stream deep learning model fo...
research
∙
01/06/2019
CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation
Convolutional neural networks (CNNs) for biomedical image analysis are o...
research
∙
12/10/2018
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
3D image segmentation plays an important role in biomedical image analys...
research
∙
06/02/2018
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
In recent years, deep learning (DL) methods have become powerful tools f...
research
∙
02/01/2018
