%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.15.23.44
%2 sid.inpe.br/sibgrapi/2021/09.15.23.44.20
%@doi 10.1109/SIBGRAPI54419.2021.00021
%T Learning to Segment Medical Images from Few-Shot Sparse Labels
%D 2021
%A Gama, Pedro Henrique Targino,
%A Oliveira, Hugo,
%A Santos, Jefersson Alex dos,
%@affiliation Universidade Federal de Minas Gerais, Brazil
%@affiliation Universidade de São Paulo, Brazil
%@affiliation Universidade Federal de Minas Gerais, Brazil
%E Paiva, Afonso ,
%E Menotti, David ,
%E Baranoski, Gladimir V. G. ,
%E Proença, Hugo Pedro ,
%E Junior, Antonio Lopes Apolinario ,
%E Papa, João Paulo ,
%E Pagliosa, Paulo ,
%E dos Santos, Thiago Oliveira ,
%E e Sá, Asla Medeiros ,
%E da Silveira, Thiago Lopes Trugillo ,
%E Brazil, Emilio Vital ,
%E Ponti, Moacir A. ,
%E Fernandes, Leandro A. F. ,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K computer vision, meta-learning, semantic segmentation, medical imaging.
%X In this paper, we propose a novel approach for few-shot semantic segmentation with sparse labeled images.We investigate the effectiveness of our method, which is based on the Model-Agnostic Meta-Learning (MAML) algorithm, in the medical scenario, where the use of sparse labeling and few-shot can alleviate the cost of producing new annotated datasets. Our method uses sparse labels in the meta-training and dense labels in the meta-test, thus making the model learn to predict dense labels from sparse ones. We conducted experiments with four Chest X-Ray datasets to evaluate two types of annotations (grid and points). The results show that our method is the most suitable when the target domain highly differs from source domains, achieving Jaccard scores comparable to dense labels, using less than 2% of the pixels of an image with labels in few-shot scenarios.
%@language en
%3 SIBGRAPI_MetaLearning_Medical.pdf