870 citations · 1.2k across the 29 of their papers we have counts for
29 papers
Evaluating Few-Shot Pill Recognition Under Visual Domain Shift
W. I. Chu, G. Tarroni, L. Li
Adverse drug events are a significant source of preventable harm, which has led to the development of automated pill recognition systems to enhance medication safety. Real-world de…
A dataset of medication images with instance segmentation masks for preventing adverse drug events
W. I. Chu, S. Hirani, G. Tarroni +1
Medication errors and adverse drug events (ADEs) pose significant risks to patient safety, often arising from difficulties in reliably identifying pharmaceuticals in real-world set…
An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration
Vasilis Siomos, Jonathan Passerat-Palmbach, Giacomo Tarroni
Federated learning is a decentralized collaborative training paradigm preserving stakeholders' data ownership while improving performance and generalization. However, statistical h…
Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection
Sergio Naval Marimont, Vasilis Siomos, Matthew Baugh +3
Unsupervised Anomaly Detection (UAD) methods aim to identify anomalies in test samples comparing them with a normative distribution learned from a dataset known to be anomaly-free.…
Sample selection with noise rate estimation in noise learning of medical image analysis
Maolin Li, Giacomo Tarroni
In the field of medical image analysis, deep learning models have demonstrated remarkable success in enhancing diagnostic accuracy and efficiency. However, the reliability of these…
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos +3
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free…
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach +1
Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data…
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Sergio Naval Marimont, Vasilis Siomos, Giacomo Tarroni
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy. An established app…
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
Sergio Naval Marimont, Giacomo Tarroni
The detection and localization of anomalies is one important medical image analysis task. Most commonly, Computer Vision anomaly detection approaches rely on manual annotations tha…
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
David Herron, Ernesto Jiménez-Ruiz, Giacomo Tarroni +1
NeSy4VRD is a multifaceted resource designed to support the development of neurosymbolic AI (NeSy) research. NeSy4VRD re-establishes public access to the images of the VRD dataset…
Implicit U-Net for volumetric medical image segmentation
Sergio Naval Marimont, Giacomo Tarroni
U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose th…
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation
Chen Chen, Chen Qin, Cheng Ouyang +7
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large m…
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…
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
Chen Chen, Cheng Ouyang, Giacomo Tarroni +4
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead…
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
Wenjia Bai, Chen Chen, Giacomo Tarroni +6
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety o…
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
Chen Chen, Carlo Biffi, Giacomo Tarroni +3
Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and func…
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni +12
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of p…
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni +4
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imag…
A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks
Giacomo Tarroni, Ozan Oktay, Matthew Sinclair +7
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath…