Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Nouboukpo, A.; Allaoui, M. L.; Allili, M. S.
Multi-scale spatial consistency for deep semi-supervised skin lesion segmentation Article de journal
Dans: Engineering Applications of Artificial Intelligence, vol. 135, 2024, ISSN: 09521976 (ISSN), (Publisher: Elsevier Ltd).
Résumé | Liens | BibTeX | Étiquettes: Deep learning, Dermatology, Image segmentation, Lesion segmentations, Medical imaging, Multi-scales, Semi-supervised, Semi-supervised learning, Skin lesion, Skin lesion segmentation, Spatial consistency, Spatially constrained mixture model, Spatially-constrained mixture models, Supervised learning, UNets, Unlabeled data
@article{nouboukpo_multi-scale_2024,
title = {Multi-scale spatial consistency for deep semi-supervised skin lesion segmentation},
author = {A. Nouboukpo and M. L. Allaoui and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195700182&doi=10.1016%2fj.engappai.2024.108681&partnerID=40&md5=e1cc2b6a1bb0aed530e8c04583c76167},
doi = {10.1016/j.engappai.2024.108681},
issn = {09521976 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {135},
abstract = {This paper introduces a novel semi-supervised framework, the Multiscale Spatial Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation. MSCNet uses local and global spatial consistency to leverage a minimal set of labeled data, supplemented by a large number of unlabeled data, to improve segmentation. The model is is based on a single Encoder–Decoder (ED) network, augmented with a Spatially-Constrained Mixture Model (SCMM) to enforce spatial coherence in predictions. To encode the local spatial consistency, a hierarchical superpixel structure is used capture local region context (LRC), bolstering the model capacity to discern fine-grained lesion details. Global consistency is enforced through the SCMM module, which uses a larger context for lesion/background discrimination. In addition, it enables efficient leveraging of the unlabeled data through pseudo-label generation. Experiments demonstrate that the MSCNet outperforms existing state-of-the-art methods in segmenting complex lesions. The MSCNet has an excellent generalization capability, offering a promising direction for semi-supervised medical image segmentation, particularly in scenarios with limited annotated data. The code is available at https://github.com/AdamaTG/MSCNet. © 2024 Elsevier Ltd},
note = {Publisher: Elsevier Ltd},
keywords = {Deep learning, Dermatology, Image segmentation, Lesion segmentations, Medical imaging, Multi-scales, Semi-supervised, Semi-supervised learning, Skin lesion, Skin lesion segmentation, Spatial consistency, Spatially constrained mixture model, Spatially-constrained mixture models, Supervised learning, UNets, Unlabeled data},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a novel semi-supervised framework, the Multiscale Spatial Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation. MSCNet uses local and global spatial consistency to leverage a minimal set of labeled data, supplemented by a large number of unlabeled data, to improve segmentation. The model is is based on a single Encoder–Decoder (ED) network, augmented with a Spatially-Constrained Mixture Model (SCMM) to enforce spatial coherence in predictions. To encode the local spatial consistency, a hierarchical superpixel structure is used capture local region context (LRC), bolstering the model capacity to discern fine-grained lesion details. Global consistency is enforced through the SCMM module, which uses a larger context for lesion/background discrimination. In addition, it enables efficient leveraging of the unlabeled data through pseudo-label generation. Experiments demonstrate that the MSCNet outperforms existing state-of-the-art methods in segmenting complex lesions. The MSCNet has an excellent generalization capability, offering a promising direction for semi-supervised medical image segmentation, particularly in scenarios with limited annotated data. The code is available at https://github.com/AdamaTG/MSCNet. © 2024 Elsevier Ltd
2.
Nouboukpo, A.; Allili, M. S.
Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints Article d'actes
Dans: A., Premaratne K. Benferhot S. Antonucci (Ed.): Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS, Florida Online Journals, University of Florida, 2021, (ISSN: 23340754).
Résumé | Liens | BibTeX | Étiquettes: Classification and clustering, Group structure, Learn+, Mixture components, Mixture modeling, Mixtures, Multilevels, Number of class, Prior-knowledge, Semi-supervised learning, Supervised learning, Unlabeled data
@inproceedings{nouboukpo_weakly_2021,
title = {Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints},
author = {A. Nouboukpo and M. S. Allili},
editor = {Premaratne K. Benferhot S. Antonucci A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131143535&doi=10.32473%2fflairs.v34i1.128490&partnerID=40&md5=21cda84d36649f4835be079ea2566717},
doi = {10.32473/flairs.v34i1.128490},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS},
volume = {34},
publisher = {Florida Online Journals, University of Florida},
abstract = {We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes the number of classes is known but does not assume any prior knowledge about the number of mixture components in each class. Therefore, our model: (1) allocates multiple mixture components to individual classes, (2) estimates automatically the number of components of each class, 3) propagates class labels to unlabelled data in a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasets show the robustness and performance of our model over state-of-the-art methods. © 2021 by the authors. All rights reserved.},
note = {ISSN: 23340754},
keywords = {Classification and clustering, Group structure, Learn+, Mixture components, Mixture modeling, Mixtures, Multilevels, Number of class, Prior-knowledge, Semi-supervised learning, Supervised learning, Unlabeled data},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes the number of classes is known but does not assume any prior knowledge about the number of mixture components in each class. Therefore, our model: (1) allocates multiple mixture components to individual classes, (2) estimates automatically the number of components of each class, 3) propagates class labels to unlabelled data in a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasets show the robustness and performance of our model over state-of-the-art methods. © 2021 by the authors. All rights reserved.