Niccolò Marini

发表

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

Manfredo Atzori, Jeroen van der Laak, Damian Podareanu, 2021, Frontiers in Computer Science.

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

Damian Podareanu, J. A. van der Laak, M. Atzori, 2022, npj Digital Medicine.

stainlib: a python library for augmentation and normalization of histopathology H&E images

Damian Podareanu, J. A. van der Laak, D. Tellez, 2022, bioRxiv.

Attention-based Interpretable Regression of Gene Expression in Histology

Mar'ia Rodr'iguez Mart'inez, Mara Graziani, H. Muller, 2022, iMIMIC@MICCAI.

Empowering digital pathology applications through explainable knowledge extraction tools

Giorgio Maria Di Nunzio, M. Atzori, Henning Müller, 2022, Journal of pathology informatics.

A DEXiRE for Extracting Propositional Rules from Neural Networks via Binarization

Jean-Paul Calbimonte, D. Calvaresi, Niccolò Marini, 2022, Electronics.

Data-driven color augmentation for H&E stained images in computational pathology

S. Marchand-Maillet, M. Atzori, Henning Müller, 2023, Journal of pathology informatics.

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

Stéphane Marchand-Maillet, Manfredo Atzori, Sebastian Otálora, 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

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

Manfredo Atzori, Sebastian Otálora, Niccolò Marini, 2021, Medical Image Anal..

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

Manfredo Atzori, Sebastian Otálora, Niccolò Marini, 2021, BMC Medical Imaging.

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

H. Müller, M. Atzori, Sebastian Otálora, 2020, ICPR Workshops.

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

Manfredo Atzori, Henning Müller, Sebastian Otálora, 2020, iMIMIC/MIL3iD/LABELS@MICCAI.

A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics

Chandler D. Gatenbee, R. Greiner, L. Latonen, 2023, Scientific data.

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

H. Müller, M. Atzori, F. Ciompi, 2021, COMPAY@MICCAI.

Modelling digital health data: The ExaMode ontology for computational pathology

Giorgio Maria Di Nunzio, M. Atzori, Henning Müller, 2023, Journal of pathology informatics.

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

M. Atzori, Marek Wodzinski, Niccolò Marini, 2024, Comput. Methods Programs Biomed..

Automated classification of celiac disease in histopathological images: a multi-scale approach

M. Atzori, Niccolò Marini, Christoph M. Friedrich, 2024, Medical Imaging.

A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics

Chandler D. Gatenbee, M. Rantalainen, S. Robertson, 2023, Scientific data.

A full pipeline to analyze lung histopathology images

M. Atzori, Niccolò Marini, Simon Püttmann, 2024, Medical Imaging.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning

M. Atzori, S. Boytcheva, Marek Wodzinski, 2024, Medical Image Anal..

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

M. Atzori, J. P. Dominguez-Morales, Niccolò Marini, 2024, Medical Image Anal..