109 citations · 109 across the 4 of their papers we have counts for
4 papers
Memory-aware curriculum federated learning for breast cancer classification
Amelia Jiménez-Sánchez, Mickael Tardy, Miguel A. González Ballester +2
For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative sample…
Lightweight U-Net for High-Resolution Breast Imaging
Mickael Tardy, Diana Mateus
We study the fully convolutional neural networks in the context of malignancy detection for breast cancer screening. We work on a supervised segmentation task looking for an accept…
Improving Mammography Malignancy Segmentation by Designing the Training Process
Mickael Tardy, Diana Mateus
We work on the breast imaging malignancy segmentation task while focusing on the training process instead of network complexity. We designed a training process based on a modified…
A closer look onto breast density with weakly supervised dense-tissue masks
Mickael Tardy, Bruno Scheffer, Diana Mateus
This work focuses on the automatic quantification of the breast density from digital mammography imaging. Using only categorical image-wise labels we train a model capable of predi…