Niccolò Marini

According to our database, Niccolò Marini authored at least 18 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.

Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr, Kimmo Kartasalo, Constance Boissin, Sonja Koivukoski, Aino Kuusela, Dusan Rasic, Yanbo Feng, Sandra Kristiane Sinius Pouplier, Abhinav Sharma, Kajsa Ledesma Eriksson, Stephanie Robertson, Christian Marzahl, Chandler D. Gatenbee, Alexander R. A. Anderson, Marek Wodzinski, Artur Jurgas, Niccolò Marini, Manfredo Atzori, Henning Müller, Daniel Budelmann, Nick Weiss, Stefan Heldmann, Johannes Lotz, Jelmer M. Wolterink, Bruno De Santi, Abhijeet Patil, Amit Sethi, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Mahtab Farrokh, Neeraj Kumar, Russell Greiner, Leena Latonen, Anne-Vibeke Laenkholm, Johan Hartman, Pekka Ruusuvuori, Mattias Rantalainen

Medical Image Anal., 2024

A systematic comparison of deep learning methods for Gleason grading and scoring.

Juan Pedro Dominguez-Morales, Lourdes Duran-Lopez, Niccolò Marini, Saturnino Vicente Diaz, Alejandro Linares-Barranco, Manfredo Atzori, Henning Müller

Medical Image Anal., 2024

Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning.

Niccolò Marini, Stefano Marchesin, Lluis Borras Ferris, Simon Püttmann, Marek Wodzinski, Riccardo Fratti, Damian Podareanu, Alessandro Caputo, Svetla Boytcheva, Simona Vatrano, Filippo Fraggetta, Iris Nagtegaal, Gianmaria Silvello, Manfredo Atzori, Henning Müller

CoRR, 2024

DeeperHistReg: Robust Whole Slide Images Registration Framework.

Marek Wodzinski, Niccolò Marini, Manfredo Atzori, Henning Müller

CoRR, 2024

RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge.

Marek Wodzinski, Niccolò Marini, Manfredo Atzori, Henning Müller

Comput. Methods Programs Biomed., 2024

2023

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans.

Selene Tomassini, Nicola Falcionelli, Giulia Bruschi, Agnese Sbrollini, Niccolò Marini, Paolo Sernani, Micaela Morettini, Henning Müller, Aldo Franco Dragoni, Laura Burattini

Comput. Medical Imaging Graph., December, 2023

Deep learning methods to reduce the need for annotations for the extraction of knowledge from multimodal heterogeneous medical data.

Niccolò Marini

PhD thesis, 2023

Explanation Generation via Decompositional Rules Extraction for Head and Neck Cancer Classification.

Victor Contreras, Andrea Bagante, Niccolò Marini, Michael Schumacher, Vincent Andrearczyk, Davide Calvaresi

Proceedings of the Explainable and Transparent AI and Multi-Agent Systems, 2023

2022

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.

Niccolò Marini, Stefano Marchesin, Sebastian Otálora, Marek Wodzinski, Alessandro Caputo, Mart van Rijthoven, Witali Aswolinskiy, John-Melle Bokhorst, Damian Podareanu, Edyta Petters, Svetla Boytcheva, Genziana Buttafuoco, Simona Vatrano, Filippo Fraggetta, Jeroen van der Laak, Maristella Agosti, Francesco Ciompi, Gianmaria Silvello, Henning Müller, Manfredo Atzori

npj Digit. Medicine, 2022

Attention-Based Interpretable Regression of Gene Expression in Histology.

Mara Graziani, Niccolò Marini, Nicolas Deutschmann, Nikita Janakarajan, Henning Müller, María Rodríguez Martínez

Proceedings of the Interpretability of Machine Intelligence in Medical Image Computing, 2022

Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images Based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge.

Marek Wodzinski, Artur Jurgas, Niccolò Marini, Manfredo Atzori, Henning Müller

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022

2021

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification.

Niccolò Marini, Sebastian Otálora, Henning Müller, Manfredo Atzori

Medical Image Anal., 2021

Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images.

Niccolò Marini, Sebastian Otálora, Damian Podareanu, Mart van Rijthoven, Jeroen van der Laak, Francesco Ciompi, Henning Müller, Manfredo Atzori

Frontiers Comput. Sci., 2021

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification.

Sebastian Otálora, Niccolò Marini, Henning Müller, Manfredo Atzori

BMC Medical Imaging, 2021

H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression.

Niccolò Marini, Manfredo Atzori, Sebastian Otálora, Stéphane Marchand-Maillet, Henning Müller

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations.

Niccolò Marini, Sebastian Otálora, Francesco Ciompi, Gianmaria Silvello, Stefano Marchesin, Simona Vatrano, Genziana Buttafuoco, Manfredo Atzori, Henning Müller

Proceedings of the MICCAI Workshop on Computational Pathology, 2021

2020

Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks.

Sebastian Otálora, Niccolò Marini, Henning Müller, Manfredo Atzori

Proceedings of the Interpretable and Annotation-Efficient Learning for Medical Image Computing, 2020

Semi-supervised Learning with a Teacher-Student Paradigm for Histopathology Classification: A Resource to Face Data Heterogeneity and Lack of Local Annotations.

Niccolò Marini, Sebastian Otálora, Henning Müller, Manfredo Atzori

Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020