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Leonardo Rundo
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2020 – today
- 2024
- [c20]Inês Prata Machado, Anna Reithmeir, Fryderyk Victor Kögl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James D. Brenton, Julia A. Schnabel, Mireia Crispin:
A Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer. WBIR 2024: 295-307 - [i19]Inês Prata Machado, Anna Reithmeir, Fryderyk Victor Kögl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James D. Brenton, Julia A. Schnabel, Mireia Crispin:
A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer. CoRR abs/2407.17114 (2024) - 2023
- [j42]Thomas Buddenkotte
, Lorena Escudero Sanchez
Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation. Comput. Biol. Medicine 163: 107096 (2023) - [j41]Carmelo Militello
CT Radiomic Features and Clinical Biomarkers for Predicting Coronary Artery Disease. Cogn. Comput. 15(1): 238-253 (2023) - [j40]Moloud Abdar
Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: A clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm. Inf. Fusion 97: 101813 (2023) - [j39]Michael Yeung
Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation. J. Digit. Imaging 36(2): 739-752 (2023) - [j38]Moloud Abdar
Hercules: Deep Hierarchical Attentive Multilevel Fusion Model With Uncertainty Quantification for Medical Image Classification. IEEE Trans. Ind. Informatics 19(1): 274-285 (2023) - 2022
- [j37]Carmelo Militello
Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering. Biomed. Signal Process. Control. 71(Part): 103113 (2022) - [j36]Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation. Comput. Medical Imaging Graph. 95: 102026 (2022) - [j35]Andronicus Ayobami Akinyelu, Fulvio Zaccagna
Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey. J. Imaging 8(8): 205 (2022) - [j34]Simone G. Riva
SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks. Symmetry 14(1): 119 (2022) - [c19]Antonio Greco, Leonardo Rundo
Imitation Learning for Autonomous Vehicle Driving: How Does the Representation Matter? ICIAP (1) 2022: 15-26 - [c18]Gennaro Percannella
Joint Intensity Classification and Specimen Segmentation on HEp-2 Images: a Deep Learning Approach. ICPR 2022: 4343-4349 - [c17]Michael Yeung, Leonardo Rundo
Focal Attention Networks: Optimising Attention for Biomedical Image Segmentation. ISBI 2022: 1-5 - [i18]Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar, Ramona Woitek, Cathal McCague
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation. CoRR abs/2209.09563 (2022) - 2021
- [j33]Changhee Han
MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction. BMC Bioinform. 22-S(2): 31 (2021) - [j32]Michael Yeung
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy. Comput. Biol. Medicine 137: 104815 (2021) - [j31]Maureen van Eijnatten
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. Comput. Methods Programs Biomed. 208: 106261 (2021) - [j30]Vincenzo Conti
A multimodal retina-iris biometric system using the Levenshtein distance for spatial feature comparison. IET Biom. 10(1): 44-64 (2021) - [j29]Carmelo Militello
A Computational Study on Temperature Variations in MRgFUS Treatments Using PRF Thermometry Techniques and Optical Probes. J. Imaging 7(4): 63 (2021) - [j28]Leonardo Rundo
Advanced Computational Methods for Oncological Image Analysis. J. Imaging 7(11): 237 (2021) - [j27]Andrea Tangherloni
FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks. PLoS Comput. Biol. 17(9) (2021) - [j26]Carmelo Militello
Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons. Symmetry 13(5): 750 (2021) - [j25]Vincenzo Conti
A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks. IEEE Trans. Emerg. Top. Comput. 9(4): 1709-1722 (2021) - [j24]Leonardo Rundo
A CUDA-powered method for the feature extraction and unsupervised analysis of medical images. J. Supercomput. 77(8): 8514-8531 (2021) - [i17]Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo:
A Mixed Focal Loss Function for Handling Class Imbalanced Medical Image Segmentation. CoRR abs/2102.04525 (2021) - [i16]Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo:
Advances in Artificial Intelligence to Reduce Polyp Miss Rates during Colonoscopy. CoRR abs/2105.07467 (2021) - [i15]Leonardo Rundo:
Computer-Assisted Analysis of Biomedical Images. CoRR abs/2106.04381 (2021) - [i14]Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang:
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation. CoRR abs/2111.00528 (2021) - [i13]Michael Yeung, Guang Yang, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo:
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation. CoRR abs/2111.00533 (2021) - [i12]Michael Yeung, Leonardo Rundo, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang:
Focal Attention Networks: optimising attention for biomedical image segmentation. CoRR abs/2111.00534 (2021) - 2020
- [j23]Leonardo Rundo
Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering. Comput. Biol. Medicine 120: 103751 (2020) - [j22]Daniela Besozzi, Luca Manzoni
Computational Intelligence for Life Sciences. Fundam. Informaticae 171(1-4): 57-80 (2020) - [j21]Leonardo Rundo
A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration. Fundam. Informaticae 171(1-4): 345-365 (2020) - [j20]Leonardo Rundo
Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine. J. Biomed. Informatics 108: 103479 (2020) - [j19]Orazio Gambino
HCI for biomedical decision-making: From diagnosis to therapy. J. Biomed. Informatics 111: 103593 (2020) - [j18]Carmelo Militello
MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation. Symmetry 12(5): 773 (2020) - [c16]Changhee Han
Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems. AIAI (2) 2020: 320-333 - [p5]Leonardo Rundo
CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study. Neural Approaches to Dynamics of Signal Exchanges 2020: 269-280 - [p4]Changhee Han, Leonardo Rundo
Infinite Brain MR Images: PGGAN-Based Data Augmentation for Tumor Detection. Neural Approaches to Dynamics of Signal Exchanges 2020: 291-303 - [i11]Changhee Han, Leonardo Rundo, Kohei Murao, Takafumi Nemoto, Hideki Nakayama:
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems. CoRR abs/2001.03923 (2020) - [i10]Maureen van Eijnatten
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. CoRR abs/2005.07545 (2020) - [i9]Changhee Han, Leonardo Rundo, Kohei Murao, Tomoyuki Noguchi, Yuki Shimahara, Zoltán Ádám Milacski, Saori Koshino, Evis Sala, Hideki Nakayama, Shin'ichi Satoh:
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction. CoRR abs/2007.13559 (2020)
2010 – 2019
- 2019
- [b1]Leonardo Rundo:
Computer-Assisted Analysis of Biomedical Images. University of Milan, Italy, 2019 - [j17]Changhee Han
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection. IEEE Access 7: 156966-156977 (2019) - [j16]Andrea Tangherloni
Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design. Appl. Soft Comput. 81 (2019) - [j15]Andrea Tangherloni
GenHap: a novel computational method based on genetic algorithms for haplotype assembly. BMC Bioinform. 20-S(4): 172:1-172:14 (2019) - [j14]Carmelo Militello
A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans. Comput. Biol. Medicine 114 (2019) - [j13]Leonardo Rundo
A novel framework for MR image segmentation and quantification by using MedGA. Comput. Methods Programs Biomed. 176: 159-172 (2019) - [j12]Leonardo Rundo
MedGA: A novel evolutionary method for image enhancement in medical imaging systems. Expert Syst. Appl. 119: 387-399 (2019) - [j11]Leonardo Rundo
USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365: 31-43 (2019) - [c15]Changhee Han, Yoshiro Kitamura, Akira Kudo
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-Based CT Image Augmentation for Object Detection. 3DV 2019: 729-737 - [c14]Changhee Han, Leonardo Rundo
GAN-Based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis. CIBB 2019: 44-54 - [c13]Changhee Han, Kohei Murao, Tomoyuki Noguchi, Yusuke Kawata, Fumiya Uchiyama, Leonardo Rundo
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images. CIKM 2019: 119-127 - [c12]Paulo Lapa, Ivo Gonçalves
Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: a study with the semantic learning machine. GECCO (Companion) 2019: 381-382 - [c11]Paulo Lapa, Ivo Gonçalves
Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI. GECCO (Companion) 2019: 1837-1845 - [c10]Leonardo Rundo
HaraliCU: GPU-Powered Haralick Feature Extraction on Medical Images Exploiting the Full Dynamics of Gray-Scale Levels. PaCT 2019: 304-318 - [p3]Leonardo Rundo
Computer-Assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis. Quantifying and Processing Biomedical and Behavioral Signals 2019: 229-241 - [i8]Changhee Han, Kohei Murao, Tomoyuki Noguchi, Yusuke Kawata, Fumiya Uchiyama, Leonardo Rundo, Hideki Nakayama, Shin'ichi Satoh:
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR images. CoRR abs/1902.09856 (2019) - [i7]Changhee Han, Leonardo Rundo, Ryosuke Araki, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi:
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection. CoRR abs/1903.12564 (2019) - [i6]Leonardo Rundo, Changhee Han, Jin Zhang, Ryuichiro Hataya, Yudai Nagano, Carmelo Militello, Claudio Ferretti, Marco S. Nobile, Andrea Tangherloni, Maria Carla Gilardi, Salvatore Vitabile, Hideki Nakayama, Giancarlo Mauri:
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study. CoRR abs/1903.12571 (2019) - [i5]Leonardo Rundo, Changhee Han, Yudai Nagano, Jin Zhang, Ryuichiro Hataya, Carmelo Militello, Andrea Tangherloni, Marco S. Nobile, Claudio Ferretti, Daniela Besozzi, Maria Carla Gilardi, Salvatore Vitabile, Giancarlo Mauri, Hideki Nakayama, Paolo Cazzaniga:
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. CoRR abs/1904.08254 (2019) - [i4]Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi:
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection. CoRR abs/1905.13456 (2019) - [i3]Changhee Han, Yoshiro Kitamura, Akira Kudo, Akimichi Ichinose, Leonardo Rundo, Yujiro Furukawa, Kazuki Umemoto, Hideki Nakayama, Yuanzhong Li:
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection. CoRR abs/1906.04962 (2019) - [i2]Changhee Han, Leonardo Rundo, Kohei Murao, Zoltán Ádám Milacski, Kazuki Umemoto, Hideki Nakayama, Shin'ichi Satoh:
GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis. CoRR abs/1906.06114 (2019) - 2018
- [j10]Leonardo Rundo
NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique. Int. J. Imaging Syst. Technol. 28(1): 21-37 (2018) - [j9]Orazio Gambino
A framework for data-driven adaptive GUI generation based on DICOM. J. Biomed. Informatics 88: 37-52 (2018) - [j8]Leonardo Rundo
GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model. Nat. Comput. 17(3): 521-536 (2018) - [c9]Marco S. Nobile, Andrea Tangherloni
Computational Intelligence for Parameter Estimation of Biochemical Systems. CEC 2018: 1-8 - [c8]Andrea Tangherloni
High Performance Computing for Haplotyping: Models and Platforms. Euro-Par Workshops 2018: 650-661 - [c7]Changhee Han, Hideaki Hayashi, Leonardo Rundo
GAN-based synthetic brain MR image generation. ISBI 2018: 734-738 - [c6]Andrea Tangherloni
GPU-Powered Multi-Swarm Parameter Estimation of Biological Systems: A Master-Slave Approach. PDP 2018: 698-705 - [p2]Leonardo Rundo
Fully Automatic Multispectral MR Image Segmentation of Prostate Gland Based on the Fuzzy C-Means Clustering Algorithm. Multidisciplinary Approaches to Neural Computing 2018: 23-37 - 2017
- [j7]Carmelo Militello
Area-based cell colony surviving fraction evaluation: A novel fully automatic approach using general-purpose acquisition hardware. Comput. Biol. Medicine 89: 454-465 (2017) - [j6]Leonardo Rundo
A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning. Comput. Methods Programs Biomed. 144: 77-96 (2017) - [j5]Leonardo Rundo
Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging. Inf. 8(2): 49 (2017) - [j4]Vincenzo Conti, Leonardo Rundo
Resource-Efficient Hardware Implementation of a Neural-based Node for Automatic Fingerprint Classification. J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. 8(4): 19-36 (2017) - [c5]Andrea Tangherloni
Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems. CEC 2017: 1940-1947 - [c4]Simone Spolaor
Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO's Performance. CIBB 2017: 92-102 - [c3]Simone Spolaor
Reboot strategies in particle swarm optimization and their impact on parameter estimation of biochemical systems. CIBCB 2017: 1-8 - [i1]Andrea Tangherloni, Simone Spolaor, Leonardo Rundo, Marco S. Nobile, Ivan Merelli, Paolo Cazzaniga, Daniela Besozzi, Giancarlo Mauri, Pietro Liò:
GenHap: A novel computational method based on genetic algorithms for haplotype assembly. PeerJ Prepr. 5: e3246 (2017) - 2016
- [j3]Leonardo Rundo
Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments. Medical Biol. Eng. Comput. 54(7): 1071-1084 (2016) - [c2]Leonardo Rundo
Neuro-Radiosurgery Treatments: MRI Brain Tumor Seeded Image Segmentation Based on a Cellular Automata Model. ACRI 2016: 323-333 - [c1]Leonardo Rundo
Multimodal medical image registration using Particle Swarm Optimization: A review. SSCI 2016: 1-8 - [p1]Leonardo Rundo
Semi-automatic Brain Lesion Segmentation in Gamma Knife Treatments Using an Unsupervised Fuzzy C-Means Clustering Technique. Advances in Neural Networks 2016: 15-26 - 2015
- [j2]Carmelo Militello
A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation. Comput. Biol. Medicine 62: 277-292 (2015) - [j1]Carmelo Militello
Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering. Int. J. Imaging Syst. Technol. 25(3): 213-225 (2015)
Coauthor Index
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last updated on 2024-11-04 20:43 CET by the dblp team
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