Schlagworte:
- 3D-color-grayscale conversion
- Alzheimer disease
- Alzheimer's disease
- artificial intelligence
- Automatic weak labels
- Biological signal acquisition
- BLS
- Chopped carbon fiber
- Classification
- Clinical decision support
- clustering
- contrastive learning
- Deep convolutional neural network
- Deep Learning
- digital pathology
- EEG
- Electromagnetic interference shielding
- Explainable AI
- Flexible sensor
- Histopathology
- Histopathology image classification
- image retrieval
- interpretability
- Longitudinal clustering
- machine learning
- MOT
- Natural Language Processing
- NIHSS
- Noisy labels
- Open access
- Parkinson's disease
- pathology classification
- Recovery ratio
- retrospective matching network
- Scanning electron microscopy
- self-supervised learning
- SEMG
- ShallowNet
- Stroke
- transformer
- UAV
- whole slide imaging
Publikationen von Manfredo Atzori sortiert nach erstem Autor
C
| , , , , , und , Hand Gesture Classification in Transradial Amputees Using the Myo Armband Classifier, in: 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Enschede, The Netherlands, Seiten 156 - 161, 2018 |
|
| , , und , Improving Robotic Hand Prosthesis Control With Eye Tracking and Computer Vision: A Multimodal Approach Based on the Visuomotor Behavior of Grasping (2022), in: Frontiers in Artificial Intelligence(199) |
|
| , , und , Eye-hand coordination to improve grasp-type recognition in hand prostheses, in: Cybathlon Symposium, Zürich, Switzerland, 2020 |
|
| , , , , , , , , und , Multi-functional control and usage of a 3D printed robotic hand prosthesis with the Myo armband by hand amputees (2018), in: BioRxiv |
|
| , und , Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances (2018), in: Journal of Rehabilitation and Assistive Technologies Engineering, 5 |
[DOI] [URL] |
| , , , und , Analyzing the trade-off between training session time and performance in myoelectric hand gesture recognition during upper limb movement, in: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, Canada, 2019 |
|
| , , , , , , , , , , , , und , Gaze, visual,myoelectric data of grasps for intelligent prosthetics (2020), in: Nature Scientific Data |
|
| , , , , , , , und , Semi-automatic training of an object recognition system in scene camera data using gaze tracking and accelerometers, in: International Conference on Computer Vision Systems (ICVS), Shenzhen (China), 2017 |
|
D
| und , Toward improving reproducibility in neuroimaging deep learning studies (2024), in: Frontiers in Neuroscience, 18(1509358) |
|
| und , Applications of Self-Supervised Learning to Biomedical Signals: a Survey (2023), in: IEEE Access |
| , , , , , , , , und , TransformEEG: Towards Improving Model Generalizability in Deep Learning-based EEG Parkinson's Disease Detection (2025), in: arXiv preprint arXiv:2507.07622 |
| , , , , und , Self-Supervised Representation Learning for EEG-Based Detection of Neurodegenerative Diseases (2025), in: APPLIED SCIENCES, 15:24(13275--13275) |
[DOI] |
| , , , und , The more, the better? Evaluating the role of EEG preprocessing for deep learning applications. (2025), in: IEEE Transactions on Neural Systems and Rehabilitation Engineering |
|
| , , , , und , The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study (2025), in: Computers in Biology and Medicine, 196(110608) |
|
| , , , und , Exploiting the PubMed Central repository to mine out a large multimodal dataset of rare cancer studies, in: SPIE Medical Imaging, Houston, TX, USA, 2020 |
|
| , , und , Classification of noisy free-text prostate cancer pathology reports using natural language processing, in: Workshop AIDP at ICPR, 2021 |
|
| , , , , , und , A systematic comparison of deep learning methods for Gleason grading and scoring (2024), in: Medical Image Analysis |
[DOI] |
F
| , und , Semantic wikis as flexible database interfaces for biomedical applications (2023), in: Scientific Reports, 13:1(1095) |
| , , , , , , , 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) |
G
| , , , , , , , , , und , Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging, in: Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis, SPIE, Seiten 74--92, 2023 |
| , , , und , Visual Cues to Improve Myoelectric Control of Upper Limb Prostheses, in: 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), Enschede, The Netherlands, Seiten 783-788, IEEE, 2018 |
[DOI] [URL] |
, , , und , Measuring Movement Classification Performance with the Movement Error Rate (2014), in: IEEE Transactions on neural systems and rehabiliation engineering
|
|
| , , , , , , , , , , , und , Megane Pro: myo-electricity, visual and gaze tracking integration as a resource for dexterous hand prosthetics, in: IEEE International Conference on Rehabilitation Robotics, London, UK, 2017 |
|
, , , , und , On the visiomotor behavior of amputees and able-bodied people during grasping, (2019), in: Frontiers in Bioengienering and Biotechnology
|
J
, und , A large calibrated database of hand movements and grasp kinematics (2020), in: Scientific Data
|
|
| , , , , und , Kinematic synergies of hand grasps, in: ESB 2019, Vienna, Austria, 2019 |
|
| , , und , Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset (2019), in: Journal of Neuroengineering and rehabilitation |
|
| , , , , , , und , Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score, in: SPIE Medical Imaging, Seiten 101400O-101400O-9, 2017 |
[DOI] [URL] |
| , , , , , , und , Elsevier book on Texture Analysis, Kapitel Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning, 2017 |
|
, , und , Deep Multimodal Case-Based Retrieval for Large Histopathology Datasets, in: MICCAI 2017 workshop on Patch-based image analysis, Quebec City, Canada, 2017
|
|
| , , , und , Artifact Augmentation for Learning-based Quality Control of Whole Slide Images, in: EMBC, Sydney, Australia, 2023 |
| , , , , und , Improving quality control of whole slide images by explicit artifact augmentation (2024), in: Scientific Reports, 14:1(17847) |
|
K
| , , und , Generalizing Convolution Neural Networks on Stain Color Heterogeneous Data for Computational Pathology, in: SPIE Medical Imaging, Houston, TX, USA,, 2020 |
|
M
| , , , , und , Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter-and Intra-Subject Study (2021), in: Sensors, 21:22(7500) |
M
| , , , , , , , , , , , , und , Empowering Digital Pathology Applications through Explainable Knowledge Extraction Tools (2022), in: Journal of Digital Pathology |
|
M
| , , , 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 |
|
| , , , , , , , , , , , , , 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] |
| , , , , , , , , , , , , , , , , , , 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 |
|
| , , , , , , , , , , , 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 Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations, in: COMPAY workshop at MICCAI, 2021 |
|
| , , 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 |
|
| , , 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 |
|
| , , , , , , und , Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images (2021), in: Frontiers in Computer Science |
|
| , , 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 |
|
| , , , , , , , , , , , und , Modelling digital health data: The ExaMode ontology for computational pathology (2023), in: PMC |
[DOI] |
| , , , , , , , , , und , Modelling digital health data: The ExaMode ontology for computational pathology (2023), in: Journal of Pathology Informatics(100332) |
| , , , , und , Biologically constrained Transformers improve stability and interpretability in single-cell transcriptomics, in: Proceedings of Bioinformatics Italian Society 2026, 2026 |
| , , , , und , Using Publicly Available Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction, in: Multimedia Modeling (MMM 2020), Seoul, Korea, 2020 |
|
| und , Combining object recognition, gaze tracking and electromyography to guide prosthetic hands: experiences from two reserch projects (2019), in: Jedlik Laboratory reports, VII:2 |
|