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 (ICPR Workshops (1), 2020)

Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks. - Sebastian Otálora; Niccolò Marini; Henning Müller; Manfredo Atzori (iMIMIC/MIL3ID/LABELS@MICCAI, 2020)

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 001; Simona Vatrano; Genziana Buttafuoco; Manfredo Atzori; Henning Müller (COMPAY@MICCAI, 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 (ICCVW, 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)

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)

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)

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 (BrainLes@MICCAI, 2022)

Attention-Based Interpretable Regression of Gene Expression in Histology. - Mara Graziani; Niccolò Marini; Nicolas Deutschmann; Nikita Janakarajan; Henning Müller; María Rodríguez Martínez (iMIMIC@MICCAI, 2022)

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. - Niccolò Marini; Stefano Marchesin 001; Sebastian Otálora; Marek Wodzinski; Alessandro Caputo; Mart van Rijthoven; Witali Aswolinskiy; John-Melle Bokhorst; Damian Podareanu; Edyta Petters; Svetla Boytcheva; Genziana Buttafuoco; et al. (npj Digit. Medicine, 2022)

Explanation Generation via Decompositional Rules Extraction for Head and Neck Cancer Classification. - Victor Contreras; Andrea Bagante; Niccolò Marini; Michael Schumacher 001; Vincent Andrearczyk; Davide Calvaresi (EXTRAAMAS, 2023)

Deep learning methods to reduce the need for annotations for the extraction of knowledge from multimodal heterogeneous medical data. - Niccolò Marini (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., 2023)

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)

DeeperHistReg: Robust Whole Slide Images Registration Framework. - Marek Wodzinski; Niccolò Marini; Manfredo Atzori; Henning Müller (CoRR, 2024)

Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning. - Niccolò Marini; Stefano Marchesin 001; Lluis Borras Ferris; Simon Püttmann; Marek Wodzinski; Riccardo Fratti; Damian Podareanu; Alessandro Caputo; Svetla Boytcheva; Simona Vatrano; Filippo Fraggetta; Iris Nagtegaal; et al. (CoRR, 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)

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning. - Niccolò Marini; Stefano Marchesin 001; Marek Wodzinski; Alessandro Caputo; Damian Podareanu; Bryan Cardenas Guevara; Svetla Boytcheva; Simona Vatrano; Filippo Fraggetta; Francesco Ciompi; Gianmaria Silvello; Henning Müller; et al. (Medical Image Anal., 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; et al. (Medical Image Anal., 2024)

The Hidden Threat of Hallucinations in Binary Chest X-Ray Pneumonia Classification. - Sivaramakrishnan Rajaraman; Zhaohui Liang; Niccolò Marini; Zhiyun Xue; Sameer K. Antani (CBMS, 2025)

Detecting Oral Cancer Using Tabular Deep Learning. - Zhiyun Xue; Zhaohui Liang; Sivaramakrishnan Rajaraman; Niccolò Marini; Sameer K. Antani (COINS, 2025)

Green AI: Which Programming Language Consumes the Most? - Niccolò Marini; Leonardo Pampaloni; Filippo Di Martino; Roberto Verdecchia; Enrico Vicario (GREENS@ICSE, 2025)

FRED: The Florence RGB-Event Drone Dataset. - Gabriele Magrini; Niccolò Marini; Federico Becattini; Lorenzo Berlincioni; Niccolò Biondi; Pietro Pala; Alberto Del Bimbo (CoRR, 2025)