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Publications

Publications by C-BER

2019

EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks for Diabetic Retinopathy Detection

Authors
Costa, P; Araujo, T; Aresta, G; Galdran, A; Mendonca, AM; Smailagic, A; Campilho, A;

Publication
2019 16th International Conference on Machine Vision Applications (MVA)

Abstract

2019

Uncertainty-Aware Artery/Vein Classification on Retinal Images

Authors
Galdran, A; Meyer, M; Costa, P; MendonCa,; Campilho, A;

Publication
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Abstract

2019

BACH: Grand challenge on breast cancer histology images

Authors
Aresta, G; Araújo, T; Kwok, S; Chennamsetty, SS; Safwan, M; Alex, V; Marami, B; Prastawa, M; Chan, M; Donovan, M; Fernandez, G; Zeineh, J; Kohl, M; Walz, C; Ludwig, F; Braunewell, S; Baust, M; Vu, QD; To, MNN; Kim, E; Kwak, JT; Galal, S; Sanchez Freire, V; Brancati, N; Frucci, M; Riccio, D; Wang, Y; Sun, L; Ma, K; Fang, J; Kone, I; Boulmane, L; Campilho, A; Eloy, C; Polónia, A; Aguiar, P;

Publication
Medical Image Analysis

Abstract
Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. © 2019 Elsevier B.V.

2019

End-to-End Ovarian Structures Segmentation

Authors
Wanderley, DS; Carvalho, CB; Domingues, A; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;

Publication
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

Abstract

2019

Learning to Segment the Lung Volume from CT Scans Based on Semi-Automatic Ground-Truth

Authors
Sousa, P; Galdran, A; Costa, P; Campilho, A;

Publication
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Abstract

2019

Real-Time Informative Laryngoscopic Frame Classification with Pre-Trained Convolutional Neural Networks

Authors
Galdran, A; Costa, P; Campilho, A;

Publication
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Abstract

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