Schlagworte:
- Automatic weak labels
- binary neural networks
- Deep Learning
- eXplainable Artificial Intelligence (XAI)
- Feature ranking
- Global explainability
- global explanations
- Histopathology image classification
- HPV status explanation. TNM explanation
- Local explainability
- Noisy labels
- rule extraction
- rules extraction
- rules induction
Publikationen von Niccolò Marini sortiert nach Titel
A
| , , , , , , und , A DEXiRE for Extracting Propositional Rules from Neural Networks via Binarization (2022), in: MDPI Electronics, 11:24 |
[DOI] [URL] |
| , , , , , , , und , A full pipeline to analyze lung histopathology images, in: Digital and Computational Pathology, SPIE Medical Imaging, 2024 |
[DOI] |
| , , , , und , A multi-scale CNN for transfer learning in sEMG-based hand gesture recognition for prosthetic devices (2024), in: Sensors, 24:22(7147) |
| , , und , A multi-task Multiple Instance Learning algorithm to analyze large whole slide images from the BRIGHT challenge 2022, in: ISBI Challenges 2022, Bangalore India, 2022 |
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| , , , , , und , A systematic comparison of deep learning methods for Gleason grading and scoring (2024), in: Medical Image Analysis |
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| , , , , und , Attention-based Interpretable Regression of Gene Expression in Histology, in: Proceedings of the The Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 2022, 2022 |
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| , , , , , , , , , , und , Automated classification of celiac disease in histopathological images: a multi-scale approach, in: Computer-Aided Diagnosis, SPIE Medical Imaging, 2024 |
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| , , , , , , , , , und , Automatic labels are as effective as manual labels in digital pathology images classification with deep learning (2025), in: Journal of Pathology Informatics(100462) |
| , , , , , , , , , , , , , und , Automatic labels are as effective as manual labels in digital pathology images classification with deep learning (2025), in: Journal of Pathology Informatics, 18(100462) |
[DOI] [URL] |
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| , , und , Combining weak and strong supervised learning improves strong supervision in Gleason pattern classification (2021), in: BMC Medical Imaging |
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| , , , , , , , , , und , Data-driven color augmentation for H\&E stained images in computational pathology (2023), in: Journal of Pathology Informatics(100183) |
E
| , , , , , , , , , , , , und , Empowering Digital Pathology Applications through Explainable Knowledge Extraction Tools (2022), in: Journal of Digital Pathology |
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| , , , , und , Explanation Generation via Decompositional Rules Extraction for Head and Neck Cancer Classification, in: Explainable and Transparent AI and Multi-Agent Systems, 2023 |
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H
| , , , und , H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression, in: ICCV 2021 workshop on Computational Challenges in Digital Pathology, 2021 |
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M
| , , , , , , , , , , , und , Modelling digital health data: The ExaMode ontology for computational pathology (2023), in: PMC |
[DOI] |
| , , , , , , , und , Multi-Scale Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations, in: COMPAY workshop at MICCAI, 2021 |
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| , , , , , , , , , , , und , Multimodal Representations of Biomedical Knowledge from Limited Training Whole Slide Images and Reports using Deep Learning (2024), in: Medical Image Analysis |
[DOI] |
| , , , , , , und , Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images (2021), in: Frontiers in Computer Science |
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O
| , , , , , , , , und , On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans, Computerized Medical Imaging and Graphics (2023), in: Comput Med Imaging Graph . |
[DOI] [URL] |
R
| , , und , RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge (2024), in: Computer Methods and Programs in Biomedicine |
[DOI] |
S
| , , und , Semi-supervised learning with a teacher-student paradigm for histopathology classification: a resource to face data heterogeneity and lack of local annotations, in: Workshop Artificial Intelligence for Digital Pathology, ICPR, Milano, Italy, 2021 |
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| , , und , Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: an experiment on histopathology image classification (2021), in: Medical Image Analysis |
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| , , und , Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks, in: MICCAI workshop Labels, Lima, Peru, 2020 |
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S
| , , , , , , und , stainlib: a python library for augmentation and normalization of histopathology H&E images (2022), in: bioArXiv |
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T
| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue (2024), in: Medical Image Analysis |
[DOI] |
U
| , , , , , , , , , , , , , , , , , , und , Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations (2022), in: Nature Partner Journal on Digital Medicine |
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| , , , und , Unsupervised Deep Network for Large Deformations followed by Instance Optimization and Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge, in: MICCAI BrainLes workshop, 2022 |
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