@article {aTemprana-Salvador22, title = {DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals - Planification, Implementation and Preliminary Results}, journal = {Diagnostics}, volume = {12}, year = {2022}, month = {03/2022}, chapter = {852}, abstract = {

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.

}, keywords = {artificial intelligence, computational pathology, deep learning, digital pathology, implementation, LIS, primary diagnosis, telepathology, workflow}, doi = {10.3390/diagnostics12040852}, url = {https://www.mdpi.com/2075-4418/12/4/852}, author = {Jordi Temprana-Salvador and Pau L{\'o}pez-Garc{\'\i}a and Josep Castellv{\'\i} Vives and Llu{\'\i}s de Haro and Eudald Ballesta and Matias Rojas Abusleme and Miquel Arrufat and Ferran Marques and Casas, J. and Carlos Gallego and Laura Pons and Jos{\'e} Luis Mate and Pedro Luis Fern{\'a}ndez and Eugeni L{\'o}pez-Bonet and Ramon Bosch and Salom{\'e} Mart{\'\i}nez and Santiago Ram{\'o}n y Cajal and Xavier Matias-Guiu} } @conference {cCombaliab, title = {Digitally Stained Confocal Microscopy through Deep Learning}, booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL 2019)}, year = {2019}, month = {07/2019}, address = {London}, abstract = {

Specialists have used confocal microscopy in the ex-vivo modality to identify tumors with\ an overall sensitivity of 96.6\% and specicity of 89.2\%. However, this technology hasn{\textquoteright}t\ established yet in the standard clinical practice because most pathologists lack the knowledge to interpret its output. In this paper we propose a combination of deep learning and\ computer vision techniques to digitally stain confocal microscopy images into H\&E-like\ slides, enabling pathologists to interpret these images without specic training. We use a\ fully convolutional neural network with a multiplicative residual connection to denoise the\ confocal microscopy images, and then stain them using a Cycle Consistency Generative\ Adversarial Network.

}, author = {Marc Combalia and Javiera P{\'e}rez-Anker and Adriana Garc{\'\i}a-Herrera and Ll{\'u}cia Alos and Ver{\'o}nica Vilaplana and Ferran Marques and Susana Puig and Josep Malvehy} } @article {aVentura, title = {Multiresolution co-clustering for uncalibrated multiview segmentation}, journal = {Signal Processing: Image Communication}, year = {2019}, abstract = {

We propose a technique for coherently co-clustering uncalibrated views of a scene\ with a contour-based representation. Our work extends the previous framework,\ an iterative algorithm for segmenting sequences with small variations, where the\ partition solution space is too restrictive for scenarios where consecutive images\ present larger variations. To deal with a more flexible scenario, we present three\ main contributions. First, motion information has been considered both for\ region adjacency and region similarity. Second, a two-step iterative architecture\ is proposed to increase the partition solution space. Third, a feasible global\ optimization that allows to jointly process all the views has been implemented.\ In addition to the previous contributions, which are based on low-level features,\ we have also considered introducing higher level features as semantic information\ in the co-clustering algorithm. We evaluate these techniques on multiview and\ temporal datasets, showing that they outperform state-of-the-art approaches.

}, keywords = {Co-clustering techniques, Image segmentation, Multiview segmentation, Object segmentation}, doi = {10.1016/j.image.2019.04.010}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0923596518302054}, author = {Ventura, C. and David Varas and Ver{\'o}nica Vilaplana and Xavier Gir{\'o}-i-Nieto and Ferran Marques} }