3 papers
cs.CV2020
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…
eess.IV2020
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…
eess.IV2019
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…