4 Result(s)
-
Chapter and Conference Paper
Multi-organ Segmentation in CT from Partially Annotated Datasets using Disentangled Learning
While deep learning models are known to be able to solve the task of multi-organ segmentation, the scarcity of fully annotated multi-organ datasets poses a significant obstacle during training. The 3D volume a...
-
Chapter and Conference Paper
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cellwise analysis. It assesses the effectiveness of gene expression p...
-
Chapter and Conference Paper
Influence of imperfect annotations on deep learning segmentation models
Convolutional neural networks are the most commonly used models for multi-organ segmentation in CT volumes. Most approaches are based on supervised learning, which means that the data used for training require...
-
Chapter and Conference Paper
Abstract: Flexible Unfolding of Circular Structures for Rendering Textbook-style Cerebrovascular Maps
Comprehensive, contiguous visualizations of the main cerebral arteries and the surrounding parenchyma offer considerable potential for improving diagnostic workflows in cerebrovascular disease. Instead of manu...
