| Nome |
# |
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Optimized Block-Based Algorithms to Label Connected Components on GPUs, file e31e124d-c8e8-987f-e053-3705fe0a095a
|
915
|
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Spaghetti Labeling: Directed Acyclic Graphs for Block-Based Connected Components Labeling, file e31e124d-c7be-987f-e053-3705fe0a095a
|
872
|
|
Towards Reliable Experiments on the Performance of Connected Components Labeling Algorithms, file e31e124d-3934-987f-e053-3705fe0a095a
|
852
|
|
Optimizing GPU-Based Connected Components Labeling Algorithms, file e31e124d-7d8f-987f-e053-3705fe0a095a
|
805
|
|
A Block-Based Union-Find Algorithm to Label Connected Components on GPUs, file e31e124d-c61c-987f-e053-3705fe0a095a
|
805
|
|
Improving Skin Lesion Segmentation with Generative Adversarial Networks, file e31e124d-5bc0-987f-e053-3705fe0a095a
|
658
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Augmenting data with GANs to segment melanoma skin lesions, file e31e124d-bc2e-987f-e053-3705fe0a095a
|
649
|
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Two More Strategies to Speed Up Connected Components Labeling Algorithms, file e31e124c-e1d3-987f-e053-3705fe0a095a
|
645
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Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval, file e31e124e-7124-987f-e053-3705fe0a095a
|
632
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Skin Lesion Segmentation Ensemble with Diverse Training Strategies, file e31e124d-be1b-987f-e053-3705fe0a095a
|
608
|
|
Optimized Connected Components Labeling with Pixel Prediction, file e31e124c-2440-987f-e053-3705fe0a095a
|
530
|
|
The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications, file e31e124e-6020-987f-e053-3705fe0a095a
|
528
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A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal, file e31e124e-e49a-987f-e053-3705fe0a095a
|
506
|
|
Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification, file e31e124e-735a-987f-e053-3705fe0a095a
|
471
|
|
Indexing of Historical Document Images: Ad Hoc Dewarping Technique for Handwritten Text, file e31e124c-e895-987f-e053-3705fe0a095a
|
445
|
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Connected Components Labeling on DRAGs, file e31e124d-5133-987f-e053-3705fe0a095a
|
435
|
|
XDOCS: An Application to Index Historical Documents, file e31e124d-1da2-987f-e053-3705fe0a095a
|
413
|
|
YACCLAB - Yet Another Connected Components Labeling Benchmark, file e31e124c-2289-987f-e053-3705fe0a095a
|
384
|
|
Historical Handwritten Text Images Word Spotting through Sliding Window HOG Features, file e31e124c-c67b-987f-e053-3705fe0a095a
|
375
|
|
A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes, file e31e124e-d135-987f-e053-3705fe0a095a
|
369
|
|
Improving Segmentation of the Inferior Alveolar Nerve through Deep Label Propagation, file e31e1250-217a-987f-e053-3705fe0a095a
|
353
|
|
A Hierarchical Quasi-Recurrent approach to Video Captioning, file e31e124d-7858-987f-e053-3705fe0a095a
|
333
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M-VAD Names: a Dataset for Video Captioning with Naming, file e31e124d-bbb3-987f-e053-3705fe0a095a
|
328
|
|
Connected Components Labeling on DRAGs: Implementation and Reproducibility Notes, file e31e124d-9e95-987f-e053-3705fe0a095a
|
324
|
|
How does Connected Components Labeling with Decision Trees perform on GPUs?, file e31e124d-b292-987f-e053-3705fe0a095a
|
324
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Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks, file e31e124e-b148-987f-e053-3705fe0a095a
|
320
|
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Improving the Performance of Thinning Algorithms with Directed Rooted Acyclic Graphs, file e31e124e-f5e5-987f-e053-3705fe0a095a
|
276
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A Deep Analysis on High Resolution Dermoscopic Image Classification, file e31e124e-fd45-987f-e053-3705fe0a095a
|
265
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A Warp Speed Chain-Code Algorithm Based on Binary Decision Trees, file e31e124e-cf85-987f-e053-3705fe0a095a
|
238
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Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes, file b8a35352-1bb7-4f76-afd5-9f77f755b951
|
225
|
|
Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes, file e31e1250-08cb-987f-e053-3705fe0a095a
|
200
|
|
A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes, file e31e124e-da75-987f-e053-3705fe0a095a
|
190
|
|
Fast Run-Based Connected Components Labeling for Bitonal Images, file e31e124f-f8d7-987f-e053-3705fe0a095a
|
186
|
|
Quest for Speed: The Epic Saga of Record-Breaking on OpenCV Connected Components Extraction, file 9c5f827c-39a9-4ba1-98c6-84ea04333082
|
180
|
|
Long-Range 3D Self-Attention for MRI Prostate Segmentation, file e31e124f-8e2a-987f-e053-3705fe0a095a
|
132
|
|
The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures, file e31e1250-7420-987f-e053-3705fe0a095a
|
128
|
|
One DAG to Rule Them All, file e31e1250-63fd-987f-e053-3705fe0a095a
|
107
|
|
Connected Components Labeling on Bitonal Images, file e31e1250-776b-987f-e053-3705fe0a095a
|
68
|
|
Ottimizzazione di Algoritmi per l’Elaborazione di Immagini Binarie, file e31e124e-6ee9-987f-e053-3705fe0a095a
|
59
|
|
Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm, file 59166227-974e-46bf-9c4d-73bb701cde8b
|
55
|
|
Artificial intelligence evaluation of confocal microscope prostate images: our preliminary experience, file 6fc34588-e96f-4aa9-9194-c3ed40453e38
|
40
|
|
Enhancing PFI Prediction with GDS-MIL: A Graph-based Dual Stream MIL Approach, file fd6dd815-9d7e-4b5e-b64d-9b405f70fcf0
|
34
|
|
Annotating the Inferior Alveolar Canal: the Ultimate Tool, file c912c77b-1d24-4b5c-9690-5aa92d6abaaf
|
7
|
|
A Graph-Based Multi-Scale Approach with Knowledge Distillation for WSI Classification, file 98da173d-7c7b-44cb-a08f-7d7a62253a7c
|
2
|
|
DAS-MIL: Distilling Across Scales for MILClassification of Histological WSIs, file ae37de2c-4693-41d6-86b8-b283d7d48f3d
|
2
|
|
ClusterFix: A Cluster-Based Debiasing Approach without Protected-Group Supervision, file 54c3faec-e99a-4b9c-b152-275e226ff995
|
1
|
| Totale |
16.274 |
