6 citations · 19 across the 5 of their papers we have counts for
5 papers
Invariance Measures for Neural Networks
Facundo Manuel Quiroga, Jordina Torrents-Barrena, Laura Cristina Lanzarini +1
Invariances in neural networks are useful and necessary for many tasks. However, the representation of the invariance of most neural network models has not been characterized. We p…
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
Carla Sendra-Balcells, Víctor M. Campello, Jordina Torrents-Barrena +17
Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower…
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures
Saul Calderon-Ramirez, Luis Oala, Jordina Torrents-Barrena +4
In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch. This work is divid…
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network
Vivek Kumar Singh, Hatem A. Rashwan, Santiago Romani +8
Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection an…
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
Vivek Kumar Singh, Santiago Romani, Hatem A. Rashwan +9
This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structur…