| Nome |
# |
|
Automated Segmentation of Cells with IHC Membrane Staining, file e384c42e-114c-d4b2-e053-9f05fe0a1d67
|
6.748
|
|
Computer-aided techniques for Chromogenic Immunohistochemistry: Status and Directions, file e384c42e-251c-d4b2-e053-9f05fe0a1d67
|
1.051
|
|
Automated segmentation of tissue images for computerized IHC analysis, file e384c42e-02f7-d4b2-e053-9f05fe0a1d67
|
892
|
|
Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation, file e384c42e-0935-d4b2-e053-9f05fe0a1d67
|
680
|
|
An automated approach to the segmentation of HEp-2 cells for the indirect immunofluorescence ANA test, file e384c42e-360e-d4b2-e053-9f05fe0a1d67
|
655
|
|
Subclass Discriminant Analysis of Morphological and Textural Features for HEp-2 Staining Pattern Classification, file e384c42e-2a29-d4b2-e053-9f05fe0a1d67
|
623
|
|
Applying Textural Features to the Classification of HEp-2 Cell Patterns in IIF images, file e384c42e-2433-d4b2-e053-9f05fe0a1d67
|
564
|
|
Classification of HEp-2 staining patterns in ImmunoFluorescence images. Comparison of Support Vector Machines and Subclass Discriminant Analysis strategies, file e384c42e-201b-d4b2-e053-9f05fe0a1d67
|
536
|
|
Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs Supervised SVM Classification, file e384c42d-fdd2-d4b2-e053-9f05fe0a1d67
|
452
|
|
ANAlyte: a modular image analysis tool for ANA testing with Indirect Immunofluorescence, file e384c42e-b0eb-d4b2-e053-9f05fe0a1d67
|
426
|
|
Motion artifact correction in ASL images: an improved automated procedure, file e384c42e-0e3b-d4b2-e053-9f05fe0a1d67
|
344
|
|
Realistic Multi-Scale Modelling of Household Electricity Behaviours, file e384c430-bc78-d4b2-e053-9f05fe0a1d67
|
117
|
|
Unsupervised HEp-2 mitosis recognition in Indirect Immunofluorescence Imaging, file e384c431-a611-d4b2-e053-9f05fe0a1d67
|
107
|
|
Colorectal Cancer Classification using Deep Convolutional Networks. An Experimental Study, file e384c431-75d3-d4b2-e053-9f05fe0a1d67
|
91
|
|
A Multi-Patient Data Driven Approach to Blood Glucose Prediction, file e384c431-0ca6-d4b2-e053-9f05fe0a1d67
|
77
|
|
DEEPrior: a deep learning tool for the prioritization of gene fusions, file e384c431-7422-d4b2-e053-9f05fe0a1d67
|
76
|
|
Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions, file e384c432-e262-d4b2-e053-9f05fe0a1d67
|
75
|
|
Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles, file e384c431-7971-d4b2-e053-9f05fe0a1d67
|
73
|
|
In-situ defect detection of metal Additive Manufacturing: an integrated framework, file e384c433-d6fe-d4b2-e053-9f05fe0a1d67
|
72
|
|
Automated 3D immunofluorescence analysis of Dorsal Root Ganglia for the investigation of neural circuit alterations: a preliminary study., file e384c431-72b0-d4b2-e053-9f05fe0a1d67
|
66
|
|
Mining textural knowledge in biological images: applications, methods and trends, file e384c431-af5e-d4b2-e053-9f05fe0a1d67
|
65
|
|
A Bayesian approach to Expert Gate Incremental Learning, file e384c433-6488-d4b2-e053-9f05fe0a1d67
|
59
|
|
A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings, file e384c431-0887-d4b2-e053-9f05fe0a1d67
|
57
|
|
A Deep Learning Approach to the Screening of Oncogenic Gene Fusions in Humans, file e384c431-995e-d4b2-e053-9f05fe0a1d67
|
53
|
|
Exploiting Gene Expression Profiles for the Automated Prediction of Connectivity between Brain Regions, file e384c431-2c22-d4b2-e053-9f05fe0a1d67
|
50
|
|
Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays, file e384c430-a5d7-d4b2-e053-9f05fe0a1d67
|
47
|
|
Cytoarchitectural analysis of the neuron-to-glia association in the dorsal root ganglia of normal and diabetic mice, file e384c432-7175-d4b2-e053-9f05fe0a1d67
|
40
|
|
null, file e384c431-e5a3-d4b2-e053-9f05fe0a1d67
|
38
|
|
Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology, file e384c431-7406-d4b2-e053-9f05fe0a1d67
|
37
|
|
BioSeqZip: a collapser of NGS redundant reads for the optimisation of sequence analysis, file e384c431-96da-d4b2-e053-9f05fe0a1d67
|
35
|
|
Image analytics and machine learning for in-situ defects detection in Additive Manufacturing, file e384c433-d402-d4b2-e053-9f05fe0a1d67
|
31
|
|
A SystemC-AMS Framework for the Design and Simulation of Energy Management in Electric Vehicles, file e384c431-c2b8-d4b2-e053-9f05fe0a1d67
|
25
|
|
Flexible on-line reconfiguration of multi-core neuromorphic platforms, file e384c431-a5ec-d4b2-e053-9f05fe0a1d67
|
23
|
|
Automated 3D immunofluorescence analysis of Dorsal Root Ganglia for the investigation of neural circuit alterations: a preliminary study., file e384c431-906d-d4b2-e053-9f05fe0a1d67
|
18
|
|
High Resolution Explanation Maps for CNNs using Segmentation Networks, file 303b95a2-8089-4d69-bd4b-356ada9023cc
|
17
|
|
Predicting the oncogenic potential of gene fusions using convolutional neural networks, file e384c431-91d5-d4b2-e053-9f05fe0a1d67
|
17
|
|
A multi-modal brain image registration framework for US-guided neuronavigation systems. Integrating MR and US for minimally invasive neuroimaging., file e384c431-72aa-d4b2-e053-9f05fe0a1d67
|
14
|
|
W2WNet: A two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality, file 5039feca-a4f8-4783-8c90-3494a8085fcb
|
11
|
|
A Novel Proof-of-concept Framework for the Exploitation of ConvNets on Whole Slide Images, file e384c433-5958-d4b2-e053-9f05fe0a1d67
|
11
|
|
Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles, file e384c431-8b65-d4b2-e053-9f05fe0a1d67
|
10
|
|
e-Pupil: IoT-based Augmentative and Alternative Communication device exploiting the pupillary near-reflex, file 49c98c38-d6da-46c2-8286-ed7a195856e4
|
8
|
|
Quality inspection of critical aircraft engine components: towards full automation, file b19a9e2d-428e-4b0f-8692-bdb618eca430
|
7
|
|
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations, file a330b4e5-c1ed-4ea2-a6d5-702adf9d3155
|
5
|
|
Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions, file e384c433-0145-d4b2-e053-9f05fe0a1d67
|
5
|
|
In-Situ Monitoring of Additive Manufacturing, file e384c433-b8d1-d4b2-e053-9f05fe0a1d67
|
5
|
|
In-Situ Monitoring of Additive Manufacturing, file e384c433-cad2-d4b2-e053-9f05fe0a1d67
|
5
|
|
Predicting the oncogenic potential of gene fusions using convolutional neural networks, file e384c431-91d4-d4b2-e053-9f05fe0a1d67
|
4
|
|
ANAlyte: a modular image analysis tool for ANA testing with Indirect Immunofluorescence, file e384c431-9d60-d4b2-e053-9f05fe0a1d67
|
4
|
|
Flexible on-line reconfiguration of multi-core neuromorphic platforms, file e384c431-c2b7-d4b2-e053-9f05fe0a1d67
|
4
|
|
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0, file 5228ba51-d54e-4536-84b6-a540541d0a75
|
3
|
|
A Data Fusion Service-Oriented Infrastructure for Production Line Monitoring, file 82de108a-926f-465f-abc1-f98ab6695462
|
3
|
|
Subclass Discriminant Analysis of Morphological and Textural Features for HEp-2 Staining Pattern Classification, file e384c42e-a324-d4b2-e053-9f05fe0a1d67
|
3
|
|
Colorectal Cancer Classification using Deep Convolutional Networks. An Experimental Study, file e384c431-7d70-d4b2-e053-9f05fe0a1d67
|
3
|
|
An automated approach to the segmentation of HEp-2 cells for the indirect immunofluorescence ANA test, file e384c431-8a89-d4b2-e053-9f05fe0a1d67
|
3
|
|
A multi-modal brain image registration framework for US-guided neuronavigation systems. Integrating MR and US for minimally invasive neuroimaging., file e384c431-9c52-d4b2-e053-9f05fe0a1d67
|
3
|
|
Cytoarchitectural analysis of the neuron-to-glia association in the dorsal root ganglia of normal and diabetic mice, file e384c432-46f4-d4b2-e053-9f05fe0a1d67
|
3
|
|
Machine learning-enabled real-time anomaly detection for electron beam powder bed fusion additive manufacturing, file 08796389-d2e8-4a9e-b540-bd39dc7365dd
|
2
|
|
Robotic Arm Dataset (RoAD): a Dataset to Support the Design and Test of Machine Learning-driven Anomaly Detection in a Production Line, file 34051fde-1055-4ee9-8fa4-fd6fea1829c1
|
2
|
|
W2WNet: A two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality, file 4d2d5fc7-e4ed-4b5b-ab66-b14bd86ab218
|
2
|
|
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0, file 5e92a931-1993-4882-9c9d-782b86092c8a
|
2
|
|
A Distributed Software Platform for Additive Manufacturing, file 8d21916f-5029-41b5-9c8d-95bf3bca59bc
|
2
|
|
A Novel Proof-of-concept Framework for the Exploitation of ConvNets on Whole Slide Images, file e384c433-826d-d4b2-e053-9f05fe0a1d67
|
2
|
|
An AI-Enabled Framework for Smart Semiconductor Manufacturing, file 1317d5d6-84bc-4019-8f16-23b1bb609188
|
1
|
|
Robotic Arm Dataset (RoAD): a Dataset to Support the Design and Test of Machine Learning-driven Anomaly Detection in a Production Line, file bf683d10-8fcf-419b-878f-383d49bb762c
|
1
|
|
A Distributed Software Platform for Additive Manufacturing, file c3df6c5f-c5f2-4b40-9831-e0baafc8beff
|
1
|
|
null, file e384c431-e5a7-d4b2-e053-9f05fe0a1d67
|
1
|
|
Image analytics and machine learning for in-situ defects detection in Additive Manufacturing, file e384c433-a99c-d4b2-e053-9f05fe0a1d67
|
1
|
|
Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays, file e384c434-5f17-d4b2-e053-9f05fe0a1d67
|
1
|
|
In-situ defect detection of metal Additive Manufacturing: an integrated framework, file e384c434-709e-d4b2-e053-9f05fe0a1d67
|
1
|
| Totale |
14.470 |
Archivio Istituzionale della Ricerca