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Found 22 papers, 10 papers with code
Domain Adaptation for Learned Image Compression with Supervised Adapters
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often outperforming traditional codecs on natural images; yet its performance may be far from optimal on images sampled from different domains.
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.
Towards Efficient Capsule Networks
From the moment Neural Networks dominated the scene for image processing, the computational complexity needed to solve the targeted tasks skyrocketed: against such an unsustainable trend, many strategies have been developed, ambitiously targeting performance's preservation.
To update or not to update? Neurons at equilibrium in deep models
Recent advances in deep learning optimization showed that, with some a-posteriori information on fully-trained models, it is possible to match the same performance by simply training a subset of their parameters.
Disentangling private classes through regularization
In this work we propose DisP, an approach for deep learning models disentangling the information related to some classes we desire to keep private, from the data processed by AI.
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.
REM: Routing Entropy Minimization for Capsule Networks
This paper proposes REM, a technique which minimizes the entropy of the parse tree-like structure, improving its explainability.
HEMP: High-order Entropy Minimization for neural network comPression
We formulate the entropy of a quantized artificial neural network as a differentiable function that can be plugged as a regularization term into the cost function minimized by gradient descent.
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
Dysplasia grading of colorectal polyps through CNN analysis of WSI
Colorectal cancer is a leading cause of cancer death for both men and women.
SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks
Deep neural networks include millions of learnable parameters, making their deployment over resource-constrained devices problematic.
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
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.
Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke
Methods: Training of the CNN was done on a subset of 100 perfusion data, while 15 samples were used as validation.
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology.
A non-discriminatory approach to ethical deep learning
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, nowadays they are used to solve an incredibly large variety of tasks.
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance loss.
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.
Post-synaptic potential regularization has potential
Improving generalization is one of the main challenges for training deep neural networks on classification tasks.
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation.
