Search Results for author:
Found 9 papers, 5 papers with code
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58. 4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months.
Contrastive learning for regression in multi-site brain age prediction
Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers.
Unbiased Supervised Contrastive Learning
In this work, we tackle the problem of learning representations that are robust to biases.
Integrating Prior Knowledge in Contrastive Learning with Kernel
To this end, we use kernel theory to propose a novel loss, called decoupled uniformity, that i) allows the integration of prior knowledge and ii) removes the negative-positive coupling in the original InfoNCE loss.
Unsupervised Learning of Unbiased Visual Representations
We propose a fully unsupervised debiasing framework, consisting of three steps: first, we exploit the natural preference for learning malignant biases, obtaining a bias-capturing model; then, we perform a pseudo-labelling step to obtain bias labels; finally we employ state-of-the-art supervised debiasing techniques to obtain an unbiased model.
EnD: Entangling and Disentangling deep representations for bias correction
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks.
Ranked #1 on
HairColor/Unbiased
on CelebA
A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19.
UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma.
Ranked #1 on
Colorectal Polyps Characterization
on UNITOPATHO
Colorectal Polyps Characterization
General Classification
+3
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community.
