Search Results for author:
Found 11 papers, 7 papers with code
Measuring the Biases and Effectiveness of Content-Style Disentanglement
In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whilst relying less on annotated data (since annotations can be tedious and costly).
Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
After training is complete, the discriminator is usually discarded, and only the generator is used for inference.
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training
At inference, the discriminator is discarded, and only the segmentor is used to predict label maps on test images.
Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning
Collecting large-scale medical datasets with fine-grained annotations is time-consuming and requires experts.
Training of a Skull-Stripping Neural Network with efficient data augmentation
Skull-stripping methods aim to remove the non-brain tissue from acquisition of brain scans in magnetic resonance (MR) imaging.
Synthetic contrast enhancement in cardiac CT with Deep Learning
In Europe the 20% of the CT scans cover the thoracic region.
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Cluster of microcalcifications can be an early sign of breast cancer.
Unsupervised Data Selection for Supervised Learning
Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing.
Controllable Image Synthesis of Industrial Data Using Stable Diffusion
Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments.
