30 citations · 40 across the 5 of their papers we have counts for
16 papers
Implicit field learning for unsupervised anomaly detection in medical images
Sergio Naval Marimont, Giacomo Tarroni
We propose a novel unsupervised out-of-distribution detection method for medical images based on implicit fields image representations. In our approach, an auto-decoder feed-forwar…
Anomaly detection through latent space restoration using vector-quantized variational autoencoders
Sergio Naval Marimont, Giacomo Tarroni
We propose an out-of-distribution detection method that combines density and restoration-based approaches using Vector-Quantized Variational Auto-Encoders (VQ-VAEs). The VQ-VAE mod…
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
Shuo Wang, Giacomo Tarroni, Chen Qin +7
In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model…
Realistic Adversarial Data Augmentation for MR Image Segmentation
Chen Chen, Chen Qin, Huaqi Qiu +6
Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learni…
Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech Processing
Eric Guizzo, Tillman Weyde, Giacomo Tarroni
We introduce the novel concept of anti-transfer learning for speech processing with convolutional neural networks. While transfer learning assumes that the learning process for a t…
Deep learning for cardiac image segmentation: A review
Chen Chen, Chen Qin, Huaqi Qiu +4
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation pap…