@conference {cHernandez22a, title = {Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours.}, booktitle = {Cancer Prevention through early detecTion (Caption) Workshop at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022)}, year = {2022}, month = {09/2022}, abstract = {

Sentinel lymph node status is a crucial prognosis factor for melanomas; nonetheless, the invasive surgery required to obtain it always puts the patient at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited datasets. We propose a pre-training strategy based on self-supervised contrastive learning to extract better image feature representations and an attention-based Multiple Instance Learning approach to enhance the model{\textquoteright}s performance. With this work, we quantitatively demonstrate that combining both methods improves various classification metrics and qualitatively show that contrastive learning encourages the network to output higher attention scores to tumour tissue and lower scores to image artifacts.

}, author = {Carlos Hernandez and Marc Combalia and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @mastersthesis {xTarres19, title = {Clasificaci{\'o}n de lesiones de piel con un ensemble de redes neuronales residuales}, year = {2019}, abstract = {

Convolutional Neural Networks have gained popularity in the recent years due to their performance regarding image analysis, both in classification and segmentation. Especially in the medical field, it is increasingly common to use automatic techniques to help specialists with the diagnosis.

In this thesis, the problem of skin lesion classification is studied. The study is based on the ISIC Challenges, given the collaboration with Hospital Cl{\'\i}nic de Barcelona, and we help in the development of the database for the ISIC Challenge 2019.

One of the key points of the development is obtaining a model that manages to classify with accuracy a database provided. To do so, we study residual neural networks and an ensemble of them to further improve the results.

The purpose of this project, therefore is the study, analysis and evaluation of the variants and modifications of residual neural networks so that it adapts to our problem using an ensemble of them. In the process, the neural network will have to tackle the problem of class imbalance

}, author = {Laia Tarr{\'e}s}, editor = {Ver{\'o}nica Vilaplana and Marc Combalia} } @inbook {bCasamitjana18, title = {Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation}, booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017}, volume = { 10670}, number = {Lecture Notes in Computer Science}, year = {2018}, pages = {381-391}, publisher = {Springer}, organization = {Springer}, edition = {Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds)}, address = {Cham}, abstract = {

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture, reformulating residual connections and making use of ROI masks to constrain the networks to train only on relevant voxels. This architecture allows dense training on problems with highly skewed class distributions, such as brain tumor segmentation, by focusing training only on the vecinity of the tumor area. We report results on BraTS2017 Training and Validation sets.

}, isbn = {978-3-319-75238-9}, issn = {978-3-319-75237-2}, doi = {https://doi.org/10.1007/978-3-319-75238-9_33}, url = {https://link.springer.com/chapter/10.1007/978-3-319-75238-9_33}, author = {Adri{\`a} Casamitjana and Marcel Cat{\`a} and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @mastersthesis {xPelegri18, title = {Clasificaci{\'o}n de im{\'a}genes histol{\'o}gicas mediante redes neuronales convolucionales}, year = {2018}, author = {Joel Bustos Pelegr{\'\i}}, editor = {Marc Combalia and Ver{\'o}nica Vilaplana} }