@inbook {bJimenez, title = {Breast Cancer Molecular Subtyping from H\&E Whole Slide Images using Foundation Models and Transformers}, booktitle = {Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care}, volume = {15451}, number = {Lecture Notes in Computer Science}, year = {2025}, publisher = {Springer}, organization = {Springer}, abstract = {

This study tackles the challenge of classifying breast cancer molecular subtypes using H\&E-stained Whole Slide Images (WSI), avoiding the cost and labor limitations of the commonly used immunohistochemistry. We leverage the Attention-Challenging Multiple Instance Learning framework and introduce a variant, ACTrans, which utilizes a transformer aggregator for improved performance. We also compare two publicly available foundation feature extractors pre-trained on large pathology datasets. A comparison of the impact of two different patch sizes at two different magnifications is made. The results obtained in our in-house dataset demonstrate that ACTrans outperforms existing methods, particularly with the UNI model at lower resolutions and larger patch sizes. In this setting, ACTrans achieves an average F1 score of 0.687, a precision of 0.755, a recall of 0.667, and an AUC of 0.812. Furthermore, these approaches enhance interpretability when displaying the attention weights. This method can potentially advance breast cancer diagnostics by leveraging the rich information within H\&E-stained WSIs.

}, author = {Lauren Jimenez and Carlos Hernandez and Ver{\'o}nica Vilaplana} } @article {aHernandeza, title = {Comparative Analysis and Interpretability of Survival Models for Melanoma Prognosis}, journal = {Computers in Biology and Medicine}, volume = {190}, year = {2025}, month = {05/2025}, abstract = {

The increasing incidence of cutaneous melanoma highlights the critical need for precision medicine to optimize treatment strategies and improve patient outcomes. This study evaluated and compared the performance and interpretability of various survival models for melanoma patients. We analyzed survival outcomes, including overall survival (OS), specific survival (SS), and disease-free survival (DFS), using the Xarxa Melanoma database, which comprises data from over 9,000 patients across 19 hospitals collected over a decade. The performance of Cox Proportional Hazards, Random Survival Forest, XGBoost, DeepSurv, and DeepHit models was assessed using the Concordance Index (C-Index), time-dependent Area Under the Curve (tdAUC), ROC-AUC, and Inverse Brier score. Our analysis showed that the Random Survival Forest outperformed the other methods, achieving C-Index scores of 0.846 for OS, 0.869 for SS, and 0.846 for DFS. Furthermore, by applying an interpretability method specifically designed for survival analysis, we identified histological subtypes associated with poor prognoses. Additionally, this study reaffirms the established importance of the Breslow index as a key prognostic factor in melanoma survival prediction.

}, issn = {0010-4825}, doi = {https://doi.org/10.1016/j.compbiomed.2025.110027}, url = {https://www.sciencedirect.com/science/article/pii/S0010482525003786?via\%3Dihub}, author = {Carlos Hernandez and Sebastian Podlipnik and Joan Ficapal and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @article {aHernandez, title = {Bcn20000: Dermoscopic lesions in the wild}, journal = {Nature - Scientific Data}, volume = {11}, year = {2024}, month = {06/2024}, abstract = {

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Cl{\'\i}nic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.

}, doi = {https://doi.org/10.1038/s41597-024-03387-w}, author = {Carlos Hernandez and Marc Combalia and Sebastian Podlipnik and Noel CF Codella and Veronica Rotemberg and Allan C Halpern and Ofer Reiter and Cristina Carrera and Alicia Barreiro and Brian Helba and Susana Puig and Ver{\'o}nica Vilaplana and Josep Malvehy} } @conference {cJimeneza, title = {Breast Cancer Molecular Subtyping from H\&E Whole Slide Images using Foundation Models and Transformers}, booktitle = {Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, MICCAI 2024}, year = {2024}, month = {10/2024}, abstract = {

This study tackles the challenge of classifying breast cancer molecular subtypes using H\&E-stained Whole Slide Images (WSI), avoiding the cost and labor limitations of the commonly used immunohistochemistry. We leverage the Attention-Challenging Multiple Instance Learning framework and introduce a variant, ACTrans, which utilizes a transformer aggregator for improved performance. We also compare two publicly available foundation feature extractors pre-trained on large pathology datasets. A comparison of the impact of two different patch sizes at two different magnifications is made. The results obtained in our in-house dataset demonstrate that ACTrans outperforms existing methods, particularly with the UNI model at lower resolutions and larger patch sizes. In this setting, ACTrans achieves an average F1 score of 0.687, a precision of 0.755, a recall of 0.667, and an AUC of 0.812. Furthermore, these approaches enhance interpretability when displaying the attention weights. This method can potentially advance breast cancer diagnostics by leveraging the rich information within H\&E-stained WSIs.

}, author = {Lauren Jimenez and Carlos Hernandez and Ver{\'o}nica Vilaplana} } @conference {cHernandezb, title = {Bridging Domains in Melanoma Diagnostics: Predicting BRAF Mutations and Sentinel Lymph Node Positivity with Attention-Based Models in Histological Images}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (DEF-AI-MIA workshop)}, year = {2024}, month = {06/2024}, abstract = {

Whole Slide Images (WSIs) have significantly advanced the field of pathology by providing highly detailed views of tissue samples. Integrating Deep Learning (DL) into this area of research particularly through transformer-based foundational models has marked a new era in automated image analysis. These foundational models are adept at extracting features from WSIs an essential step in their analysis process. The subsequent application of weakly supervised learning techniques combines these features to predict critical biomarkers such as BRAF mutations and sentinel lymph node (SLN) biopsy positivity which are vital in guiding patient treatment strategies. However the limited availability of labelled datasets in pathology hinders the usefulness of DL models. Domain adaptation strategies adeptly overcome this hurdle enabling model knowledge transfer between different tissue types thus addressing data scarcity. Our study employs a form of domain adaptation by fine-tuning two DINOv2 models one pre-trained on natural images and the other on WSI of colorectal cancer from the TCGA dataset adapting them for melanoma analysis. We also incorporate a comparison with features extracted by a third DINOv1 model trained solely on WSIs of breast cancer. With this approach we find some notable success in detecting BRAF mutations. Nonetheless predicting SLN positivity presents a more intricate challenge largely due to the indirect correlation between local histopathological features in WSIs of primary tumours and lymph node metastasis manifestation.\ 

}, author = {Carlos Hernandez and Lauren Jimenez and Ver{\'o}nica Vilaplana} } @phdthesis {dHernandez24, title = {Implementation of personalized medicine in malignant melanoma patients aided by artificial intelligence}, volume = {PhD}, year = {2024}, month = {11/2024}, type = {PhD Thesis}, abstract = {

Skin cancer, characterized by the uncontrolled growth and spread of abnormal skin cells, remains a significant global health challenge. It predominantly appears on sun-exposed areas like the face, neck, and arms, though it can also develop in less exposed regions. Melanoma, the most aggressive form of skin cancer, is particularly concerning due to its rapid metastasis if not detected early. This aggressive nature underscores the need for timely diagnosis and treatment. While advances have been made in understanding skin cancer mechanisms, the extensive surface area of the skin, as the body{\textquoteright}s largest organ, makes it easy for lesions to go undetected. However, the advent of prospective databases of clinical and imaging data has equipped the medical community with highly sensitive and specific biomarkers for cancer diagnosis. These biomarkers facilitate early detection and accurate diagnosis through non-invasive methods, allowing for effective treatment of melanoma before it spreads. This thesis introduces tools for analyzing melanoma using clinical data and Whole Slide Imaging (WSI). It focuses on identifying early-stage melanoma patients through risk grouping and biomarker detection. By applying advanced survival analysis, pattern recognition techniques, and statistical clustering, we develop predictive and interpretability models aimed at improving early detection and diagnosis. The main outcomes of this thesis are five-fold. Firstly, we evaluate various survival analysis algorithms on cutaneous melanoma datasets, identifying the most effective methods. Our analysis reveals that, to date, treebased methods still surpass deep learning models in performance on survival analysis datasets Secondly, we introduce a Python library, SurvLIMEpy, to enhance model explainability for time-to-event data, demonstrating its validity through experiments with both simulated and large-scale melanoma datasets. Thirdly, our analysis of feature importance in the trained models reveals that the features identified by SurvLIMEpy align closely with clinically relevant features reported in medical literature. Fourthly, we show that machine learning models for patient stratification outperform the AJCC staging system when combined with Quantile-Based Survival Clustering. Finally, we explore the use of AI to predict biomarker status from WSIs, utilizing selfsupervised feature extractors and Multiple Instance Learning. The concluding sections discuss the effectiveness of the methodologies employed, their benefits, and their potential impact on the treatment of skin cancer patients while also outlining future research directions to address this critical medical and scientific challenge.

}, author = {Carlos Hernandez}, editor = {Ver{\'o}nica Vilaplana} } @inbook {bHernandez, title = {Interpreting Machine Learning Models for Survival Analysis: A Study of Cutaneous Melanoma Using the SEER Database}, booktitle = {Explainable Artificial Intelligence and Process Mining Applications for Healthcare,}, volume = {2020}, number = {Communications in Computer and Information Science}, year = {2024}, publisher = {Springer, Cham}, organization = {Springer, Cham}, abstract = {

In this study, we train and compare three types of machine learning algorithms for Survival Analysis: Random Survival Forest, DeepSurv and DeepHit, using the SEER database to model cutaneous malignant melanoma. Additionally, we employ SurvLIMEpy library, a Python package designed to provide explainability for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Proportional Hazards Model. Our work underscores the importance of explainability methods for interpreting black-box models and provides insights into important features related to melanoma prognosis.

}, isbn = {978-3-031-54302-9}, issn = {978-3-031-54303-6}, doi = {https://doi.org/10.1007/978-3-031-54303-6_6}, author = {Carlos Hernandez and Cristian Pach{\'o}n-Garc{\'\i}a and Pedro Delicado and Ver{\'o}nica Vilaplana} } @article {aPachon-Garcia, title = {SurvLIMEpy: A Python package implementing SurvLIME}, journal = {Expert Systems With Applications}, volume = {237, Part C}, year = {2024}, month = {03/2024}, abstract = {

In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. Our\ implementation takes advantage of the parallelisation paradigm as all computations are performed in a matrix-wise fashion which speeds up execution time. Additionally, SurvLIMEpy assists the user with visualization tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of experiments are presented in this paper. First, by means of simulated data, we study the ability of the algorithm to capture the importance of the features. Second, we use three open source survival datasets together with a set of survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied to different models.

}, keywords = {eXplainable Artificial Intelligence, Interpretable Machine Learning, machine learning, python, Survival Analysis}, author = {Cristian Pach{\'o}n-Garc{\'\i}a and Carlos Hernandez and Pedro Delicado and Ver{\'o}nica Vilaplana} } @conference {cHernandez23, title = {Interpreting Machine Learning models for Survival Analysis: A study of Cutaneous Melanoma using the SEER Database}, booktitle = {XAI-Healthcare 2023 Workshop at 21st International Conference of Artificial Intelligence in Medicine (AIME 2023)}, year = {2023}, month = {06/2023}, address = {Portoroz, Slovenia}, author = {Carlos Hernandez and Cristian Pach{\'o}n-Garc{\'\i}a and Pedro Delicado and Ver{\'o}nica Vilaplana} } @conference {cCombaliae, title = {Artificial intelligence to predict positivity of sentinel lymph node biopsy in melanoma patients}, booktitle = {European Association of Dermato Oncology (EADO 2022)}, year = {2022}, month = {04/2022}, author = {Marc Combalia and Sebastian Podlipnik and Carlos Hernandez and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Ver{\'o}nica Vilaplana and Josep Malvehy} } @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} } @conference {cHernandeza, title = {Sentinel lymph node status prediction using self-attention networks and contrastive learning from routine histology images of primary tumours}, booktitle = {Medical Imaging with Deep Learning MIDL 2022}, year = {2022}, month = {07/2022}, abstract = {

Deep learning-based computational pathology approaches are becoming increasingly prominent in histopathology image analysis. However, these images typically come with drawbacks that hamper automatic analysis, which include: labeled sample scarcity or the extremely large size of the images (ranging from\ \ to\ \ pixels). Nonetheless, they have proven to be a powerful tool for diagnosis and risk prevention. One such prevention is reducing the number of patients who undergo surgeries that do not benefit them. This study develops a pipeline for predicting sentinel lymph node (SLN) metastasis non-invasively from digitised Whole Slide Images (WSI) of primary melanoma tumours. Furthermore, we combine the use of a weakly supervised architecture with self-supervised contrastive pre-training. We experimentally demonstrate that 1) the use of self-attention improves sentinel lymph node status prediction and 2) self-supervised contrastive learning improves the quality of the learned representations compared to a standard ImageNet pre-training, which boosts the model{\textquoteright}s performance.

}, author = {Carlos Hernandez and Marc Combalia and Josep Malvehy and Ver{\'o}nica Vilaplana} } @conference {cHernandez22, title = {Sentinel lymph node status prediction with self-attention neural networks using histologies of primary melanoma tumours}, booktitle = {European Association of Dermato Oncology (EADO 2022)}, year = {2022}, month = {04/2022}, author = {Carlos Hernandez and Ver{\'o}nica Vilaplana and Marc Combalia and Sergio Garc{\'\i}a and Sebastian Podlipnik and Julio Burgos and Susana Puig and Josep Malvehy} } @conference {cHernandez, title = {Implementation of personalized medicine in cutaneous melanoma patients aided by artificial intelligence}, booktitle = {10th World Congress of2 Melanoma / 17th EADO Congress}, year = {2021}, month = {04/2021}, author = {Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Sebastian Podlipnik and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cPodlipnik21, title = {Personalized medicine in melanoma patients aided by artificial intelligence}, booktitle = {Clinical Translation of Medical Image Computing and Computer Assisted Interventions (CLINICCAI) Workshop at MICCAI}, year = {2021}, month = {09/2021}, abstract = {

The 8th Edition of the American Joint Committee on Cancer (AJCC) staging system1 is the current standard for classifying patients into prognostic and treatment groups. This classification is used to predict the evolution of the patient, and therefore treatment actions provided to the individual. However, patients at the same stage behave differently, indicating that the current classification system is often insufficient to provide a customized prognosis for each patient2. It is, therefore, necessary to improve patient classification into prognostic groups. Furthermore, patients{\textquoteright} systemic and surgical treatments often involve significant toxicities and morbidities that impact their quality of life (i.e., sentinel node biopsy is not needed for 80\% of the melanoma patients, 50\% of patients do not benefit from adjuvant treatment)3. Therefore, melanoma patients should benefit from a more precise risk estimation.

We create a survival dataset for melanoma risk estimation and train survival XGBoost algorithms4 to predict the mortality, relapse, and metastasis risk. We compare their performance to the AJCC 2018 risk stratification system. Furthermore, we train classifiers to predict the risk of a positive lymph node biopsy and distant metastasis on melanoma patients and compare the performance of the proposed system to the clinical practice.

}, author = {Sebastian Podlipnik and Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} }