31 citations · 61 across the 6 of their papers we have counts for
6 papers
MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini +4
With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data…
Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
Niharika S. D'Souza, Hongzhi Wang, Andrea Giovannini +4
In a complex disease such as tuberculosis, the evidence for the disease and its evolution may be present in multiple modalities such as clinical, genomic, or imaging data. Effectiv…
Learning-based Defect Recognition for Quasi-Periodic Microscope Images
Nik Dennler, Antonio Foncubierta-Rodriguez, Titus Neupert +1
Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device. Defect a…
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Pushpak Pati, Antonio Foncubierta-Rodriguez, Orcun Goksel +1
Mitotic counting is a vital prognostic marker of tumor proliferation in breast cancer. Deep learning-based mitotic detection is on par with pathologists, but it requires large labe…
Towards Explainable Graph Representations in Digital Pathology
Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez +6
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics. Recently, graph techniques encod…
From visual words to a visual grammar: using language modelling for image classification
Antonio Foncubierta-Rodríguez, Henning Müller, Adrien Depeursinge
The Bag--of--Visual--Words (BoVW) is a visual description technique that aims at shortening the semantic gap by partitioning a low--level feature space into regions of the feature…