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Found 12 papers, 3 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.
Character Recognition in Byzantine Seals with Deep Neural Networks
A first deep convolutional neural network (CNN) detects characters in the seal (character localization).
Find the Lady: Permutation and Re-Synchronization of Deep Neural Networks
We advance a method to re-synchronize the order of permuted neurons.
Lung nodules segmentation from CT with DeepHealth toolkit
The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation.
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.
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
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.
DR2S : Deep Regression with Region Selection for Camera Quality Evaluation
In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition.
Feature Fusion for Robust Patch Matching With Compact Binary Descriptors
We propose a convolutional network framework for learning binary patch descriptors where pixel domain features are fused with features extracted from the transformed domain.
Learning Sparse Neural Networks via Sensitivity-Driven Regularization
The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications.
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.
