Search Results - "Marini, Niccolò"

Empowering digital pathology applications through explainable knowledge extraction tools by Marchesin, Stefano, Giachelle, Fabio, Marini, Niccolò, Atzori, Manfredo, Boytcheva, Svetla, Buttafuoco, Genziana, Ciompi, Francesco, Di Nunzio, Giorgio Maria, Fraggetta, Filippo, Irrera, Ornella, Müller, Henning, Primov, Todor, Vatrano, Simona, Silvello, Gianmaria

“…Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is…”
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Modelling digital health data: The ExaMode ontology for computational pathology by Menotti, Laura, Silvello, Gianmaria, Atzori, Manfredo, Boytcheva, Svetla, Ciompi, Francesco, Di Nunzio, Giorgio Maria, Fraggetta, Filippo, Giachelle, Fabio, Irrera, Ornella, Marchesin, Stefano, Marini, Niccolò, Müller, Henning, Primov, Todor

“…Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for…”
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Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification by Marini, Niccolò, Otálora, Sebastian, Müller, Henning, Atzori, Manfredo

“…•Improved classification performance on several prostate datasets using pseudo-labels•Generalization of the models on several high heterogeneous datasets•Few…”
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RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge by Wodzinski, Marek, Marini, Niccolò, Atzori, Manfredo, Müller, Henning

“…The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary…”
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Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification by Otálora, Sebastian, Marini, Niccolò, Müller, Henning, Atzori, Manfredo

“…One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly,…”
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A systematic comparison of deep learning methods for Gleason grading and scoring by Dominguez-Morales, Juan P., Duran-Lopez, Lourdes, Marini, Niccolò, Vicente-Diaz, Saturnino, Linares-Barranco, Alejandro, Atzori, Manfredo, Müller, Henning

“…Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that…”
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Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning by Marini, Niccolò, Marchesin, Stefano, Wodzinski, Marek, Caputo, Alessandro, Podareanu, Damian, Guevara, Bryan Cardenas, Boytcheva, Svetla, Vatrano, Simona, Fraggetta, Filippo, Ciompi, Francesco, Silvello, Gianmaria, Müller, Henning, Atzori, Manfredo

“…The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains…”
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On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans by Tomassini, Selene, Falcionelli, Nicola, Bruschi, Giulia, Sbrollini, Agnese, Marini, Niccolò, Sernani, Paolo, Morettini, Micaela, Müller, Henning, Dragoni, Aldo Franco, Burattini, Laura

“…Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not…”
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Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations by Marini, Niccolò, Marchesin, Stefano, Otálora, Sebastian, Wodzinski, Marek, Caputo, Alessandro, van Rijthoven, Mart, Aswolinskiy, Witali, Bokhorst, John-Melle, Podareanu, Damian, Petters, Edyta, Boytcheva, Svetla, Buttafuoco, Genziana, Vatrano, Simona, Fraggetta, Filippo, van der Laak, Jeroen, Agosti, Maristella, Ciompi, Francesco, Silvello, Gianmaria, Muller, Henning, Atzori, Manfredo

“…The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer…”
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Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images by Marini, Niccolò, Otálora, Sebastian, Podareanu, Damian, van Rijthoven, Mart, van der Laak, Jeroen, Ciompi, Francesco, Müller, Henning, Atzori, Manfredo

“…Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing…”
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A Multi-Scale CNN for Transfer Learning in sEMG-Based Hand Gesture Recognition for Prosthetic Devices by Fratti, Riccardo, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Tiengo, Cesare, Bassetto, Franco

“…Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring…”
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Data-driven color augmentation for H&E stained images in computational pathology by Marini, Niccolò, Otalora, Sebastian, Wodzinski, Marek, Tomassini, Selene, Dragoni, Aldo Franco, Marchand-Maillet, Stephane, Morales, Juan Pedro Dominguez, Duran-Lopez, Lourdes, Vatrano, Simona, Müller, Henning, Atzori, Manfredo

“…Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with…”
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The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue by Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Robertson, Stephanie, Marzahl, Christian, Gatenbee, Chandler D., Anderson, Alexander R.A., Wodzinski, Marek, Jurgas, Artur, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Budelmann, Daniel, Weiss, Nick, Heldmann, Stefan, Lotz, Johannes, Wolterink, Jelmer M., De Santi, Bruno, Patil, Abhijeet, Sethi, Amit, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Farrokh, Mahtab, Kumar, Neeraj, Greiner, Russell, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, Rantalainen, Mattias

“…•largest study to date on multi-stain histopathology image registration.•tissue originates from routine clinical workflows.•the contributions of eight teams…”
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Data-driven color augmentation for H E stained images in computational pathology by Niccolò Marini, Sebastian Otalora, Marek Wodzinski, Selene Tomassini, Aldo Franco Dragoni, Stephane Marchand-Maillet, Juan Pedro Dominguez Morales, Lourdes Duran-Lopez, Simona Vatrano, Henning Müller, Manfredo Atzori

“…Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with…”
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RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge by Wodzinski, Marek, Marini, Niccolò, Atzori, Manfredo, Müller, Henning

“…Computer Methods and Programs in Biomedicine, Vol. 250, 2024 The automatic registration of differently stained whole slide images (WSIs) is crucial for…”
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DeeperHistReg: Robust Whole Slide Images Registration Framework by Wodzinski, Marek, Marini, Niccolò, Atzori, Manfredo, Müller, Henning

“…DeeperHistReg is a software framework dedicated to registering whole slide images (WSIs) acquired using multiple stains. It allows one to perform the…”
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Attention-based Interpretable Regression of Gene Expression in Histology by Graziani, Mara, Marini, Niccolò, Deutschmann, Nicolas, Janakarajan, Nikita, Müller, Henning, Martínez, María Rodríguez

“…Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient…”
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H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression by Marini, Niccolo, Atzori, Manfredo, Otalora, Sebastian, Marchand-Maillet, Stephane, Muller, Henning

“…Computational pathology is a domain that aims to develop algorithms to automatically analyze large digitized histopathology images, called whole slide images…”
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Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning by Marini, Niccolò, Marchesin, Stefano, Ferris, Lluis Borras, Püttmann, Simon, Wodzinski, Marek, Fratti, Riccardo, Podareanu, Damian, Caputo, Alessandro, Boytcheva, Svetla, Vatrano, Simona, Fraggetta, Filippo, Nagtegaal, Iris, Silvello, Gianmaria, Atzori, Manfredo, Müller, Henning

“…The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples. Currently, one of…”
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The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue by Weitz, Philippe, Valkonen, Masi, Solorzano, Leslie, Carr, Circe, Kartasalo, Kimmo, Boissin, Constance, Koivukoski, Sonja, Kuusela, Aino, Rasic, Dusan, Feng, Yanbo, Pouplier, Sandra Sinius, Sharma, Abhinav, Eriksson, Kajsa Ledesma, Robertson, Stephanie, Marzahl, Christian, Gatenbee, Chandler D, Anderson, Alexander R. A, Wodzinski, Marek, Jurgas, Artur, Marini, Niccolò, Atzori, Manfredo, Müller, Henning, Budelmann, Daniel, Weiss, Nick, Heldmann, Stefan, Lotz, Johannes, Wolterink, Jelmer M, De Santi, Bruno, Patil, Abhijeet, Sethi, Amit, Kondo, Satoshi, Kasai, Satoshi, Hirasawa, Kousuke, Farrokh, Mahtab, Kumar, Neeraj, Greiner, Russell, Latonen, Leena, Laenkholm, Anne-Vibeke, Hartman, Johan, Ruusuvuori, Pekka, Rantalainen, Mattias

“…The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep…”
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