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
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| , , , , , , , , und , TransformEEG: Towards improving model generalizability in deep learning-based EEG Parkinson’s disease detection (2025), in: Elsevier |
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, , , und , Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology (2019), in: Frontiers in Bioengineering and Biotechnology-Bioinformatics and Computational Biology
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| , , und , Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content, in: MICCAI 2018 - Computational Pathology Workshop (COMPAY), Granada, Spain, 2018 |
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| , , , , , , und , Identification and retrieval of prostate cancer cases using a content-based search tool (2019), in: Pathology Informatics |
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| , , , , , , , und , Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Seiten 581-588, Springer International Publishing, 2015 |
[DOI] |
| , , , , und , A systematic comparison of deep learning strategies for weakly supervised Gleason grading,, in: SPIE Medical Imaging, Houstonm, TX, USA, 2020 |
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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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| , , und , Combining weak and strong supervised learning improves strong supervision in Gleason pattern classification (2021), in: BMC Medical Imaging |
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| , , , , , , und , stainlib: a python library for augmentation and normalization of histopathology H&E images (2022), in: bioArXiv |
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| , , , , und , Determining the scale of image patches using a deep learning approach, in: IEEE International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA, 2018 |
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| , , , und , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2018), in: BioArXiv |
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| , , und , Variability of Muscle Synergies in Hand Grasps: Analysis of Intra-and Inter-Session Data (2020), in: Sensors, 20:15(4297) |
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| , , , und , An Augmented Reality Environment to Provide Visual Feedback to Amputees during sEMG Data Acquisitions, in: TAROS (Towards Autonomous Robotic Systems), London, United Kingdom, 2019 |
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| , , , , und , Analysis of the repeatability of grasp recognition for hand robotic prosthesis control based on sEMG data, in: IEEE International Conference on Rehabilitation Robotics, London, UK,, 2017 |
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, , , , und , Comparison of Six Electromyography Acquisition Setups on Hand Movement Classification Tasks (2017), in: Plos One
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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 , Enhanced EMG-based hand gesture classification in real-world scenarios: Mitigating dynamic factors with tempo-spatial wavelet transform and deep learning (2024), in: IEEE Transactions on Medical Robotics and Bionics, 6:3(1202--1211) |
| , , , und , Enhancing Gesture Classification Using Active EMG Band and Advanced Feature Extraction Technique (2023), in: IEEE Sensors Journal |
| , , , und , Active Surface Electromyogram (sEMG) Electrodes band with Enhanced Features based technique for Hand Gesture Classification (2023) |
| , , , , und , Explainable AI-guided optimization of EMG channels and features for precise hand gesture classification: a SHAP-based study (2024), in: IEEE Transactions on Medical Robotics and Bionics |
| , , , , , , , und , Tumor proliferation grading from whole slide images, in: SPIE Medical Imaging, Houston, Texas, USA, 2018 |
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| , und , A comparative study of deep convolutional neural networks for the analysis of retinal damage in optical coherence tomography, in: Imaging Informatics for Healthcare, Research, and Applications, 2024 |
[DOI] |
| , , , , , und , Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview (2024), in: MDPI Diagostics |
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| , , und , An Overview of Public Retinal Optical Coherence Tomography Datasets: Access, Annotations, and Beyond (2024), in: Medical Informatics Europe 2024(1664 - 1668) |
[DOI] |
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| , , , , und , Bodily obstacles in mental imagery. Specific perspective-dependent effects of the apparent motion perception of an actors’ body parts (2016), in: 12th Symposium of the Zurich Center for Integrative Human Physiology (ZIHP) |
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| , , , , , , , , und , Gaze, behavioral, and clinical data for phantom limbs after hand amputation from 15 amputees and 29 controls (2020), in: Scientific Data, 7:1(1--14) |
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| , , , , , , , , und , Aiming with varying degrees of accuracy requirements: Fitt’s Law and associated gaze behaviour in upper limb amputees, in: Urobody conference, Bremen, Germany, 2018 |
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| , , , , , , , , , und , Apparent motion perception in upper limb amputees with phantom sensations: obstacle shunning and obstacle tolerance, in: Proceedings of the ACNS conference 2018, Melbourne, Australia, 2018 |
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| , , , , , , und , Obstacles to mental imagery: How bodily constraints affect apparent motion perception of human body parts. A proposed experiment., in: International Brain-Computer Interface (BCI) Meeting, Darmstadt, 2016 |
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| , , , , , , , , , , und , Apparent motion perception in upper limb amputees with phantom sensations: obstacle shunning and obstacle tolerance, in: Hand, Brain and Technology: The Somatosensory System, Monte Verità, Switzerland, 2018 |
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| , , und , Mapping of the Upper Limb Work-Space: Benchmarking Four Wrist Smoothness Metrics (2022), in: Applied Sciences, 12:24(12643) |
| , , , und , Muscle Synergy Analysis of a Hand-Grasp Dataset: a limited subset of motor modules may underlie a large variety of grasps (2018), in: Frontiers in Neurorobotics |
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| , , , , und , Functional Synergies Applied to a Publicly Available Dataset of Hand Grasps Show Evidence of Kinematic-Muscular Synergistic Control (2023), in: IEEE Access, Volume 11(108544 - 108560) |
[DOI] |
| , , , und , Deep learning based retrieval system for gigapixel histopathology cases and open access literature (2019), in: Pathology Informatics |
[DOI] |
| , , , , und , Live ECG Readings Using Google Glass in Emergency Situations, in: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milano, Italy, 2015 |
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, , , , und , A Quantitative Taxonomy of Human Hand Grasps, (2019), in: Journal of NeuroEngineering and Rehabilitation,, 16:28
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| , , , und , Quantitative hierarchical representation and comparison of hand grasps from electromyography and kinematic data, in: Workshop Learning Applications for Intelligent Autonomous Robots, 15th International Conference on Intelligent Autonomous Systems, Baden-Baden, Germany, 2018 |
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| , , , , , , und , Myo electricity and 3D printing: a resource for revolutionary hand prosthetics, in: FESSH, Berlin, Germany, 2019 |
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| , , , , , , , und , Myo electricity and 3D printing: a resource for revolutionary hand prosthetics, in: FESSH, Berlin (Germany), 2019 |
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| , , und , An Overview of Open Source Deep Learning-Based Libraries for Neuroscience (2023), in: Applied Sciences, 13:9(5472) |
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| , , und , TokenUNet: A new case for transformers integration in efficient and interpretable 3D UNets for brain imaging segmentation (2026), in: PloS one, 21:8(e0354511) |
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| , , , , , und , Behavioral Clusters and Lesion Distributions in Ischemic Stroke, Based on NIHSS Similarity Network (2025), in: Journal of Healthcare Informatics Research(1--36) |
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| , , , , , und , Behavioral Clusters in Ischemic Stroke based on NIHSS Similarity (2023), in: medRxiv(2023--11) |
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| , , , , , , , , , und , Multimodal Schizophrenia Detection by Multiclassification Analysis, in: Proceedings of the Iberoamerican Congress on Pattern Recognition 2011, Pucón, Chile, Seiten 491-498, 2011 |
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| , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , und , The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue (2024), in: Medical Image Analysis |
[DOI] |
| , , , , und , Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading, in: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC), IEEE, Seiten 1758--1761, 2020 |
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| , , , , und , Training a deep neural network for small and highly heterogeneous MRID datasets for cancer grading, in: EMBC Conference, IEEE, 2020 |
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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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| , , 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] |
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| , , , und , xEEGNet: towards explainable AI in EEG dementia classification (2025), in: JOURNAL OF NEURAL ENGINEERING, 22:4 |
[DOI] |