Search Results for author:
Found 7 papers, 3 papers with code
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph.
Unsupervised Brain Tumor Segmentation with Image-based Prompts
Specifically, instead of directly training a model for brain tumor segmentation with a large amount of annotated data, we seek to train a model that can answer the question: is a voxel in the input image associated with tumor-like hyper-/hypo-intensity?
One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation
However, accurate segmentation of novel WM tracts can still be challenging in the one-shot setting, where only one scan is annotated for the novel WM tracts.
Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations
Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples.
Benefits of Linear Conditioning with Metadata for Image Segmentation
This metadata is usually disregarded by image segmentation methods.
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation.
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data.
