Publications

Publications by Aurélio Campilho

2019

LNDb: A Lung Nodule Database on Computed Tomography

Authors
Pedrosa, J; Aresta, G; Ferreira, C; Rodrigues, M; Leitão, P; Carvalho, AS; Rebelo, J; Negrão, E; Ramos, I; Cunha, A; Campilho, A;

Publication
CoRR

Abstract

2017

Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis

Authors
Galdran, Adrian; Gila, AitorAlvarez; Meyer, MariaInes; Saratxaga, CristinaLopez; Araujo, Teresa; Garrote, Estibaliz; Aresta, Guilherme; Costa, Pedro; Mendonça, AnaMaria; Campilho, AurelioJ.C.;

Publication
CoRR

Abstract

2017

Towards Adversarial Retinal Image Synthesis

Authors
Costa, Pedro; Galdran, Adrian; Meyer, MariaInes; Abràmoff, MichaelDavid; Niemeijer, Meindert; Mendonça, AnaMaria; Campilho, Aurelio;

Publication
CoRR

Abstract

2023

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Authors
Graham, S; Vu, QD; Jahanifar, M; Weigert, M; Schmidt, U; Zhang, W; Zhang, J; Yang, S; Xiang, J; Wang, X; Rumberger, JL; Baumann, E; Hirsch, P; Liu, L; Hong, C; Avilés Rivero, AI; Jain, A; Ahn, H; Hong, Y; Azzuni, H; Xu, M; Yaqub, M; Blache, MC; Piégu, B; Vernay, B; Scherr, T; Böhland, M; Löffler, K; Li, J; Ying, W; Wang, C; Kainmueller, D; Schönlieb, CB; Liu, S; Talsania, D; Meda, Y; Mishra, P; Ridzuan, M; Neumann, O; Schilling, MP; Reischl, M; Mikut, R; Huang, B; Chien, HC; Wang, CP; Lee, CY; Lin, HK; Liu, Z; Pan, X; Han, C; Cheng, J; Dawood, M; Deshpande, S; Saad Bashir, RM; Shephard, A; Costa, P; Nunes, JD; Campilho, A; Cardoso, JS; S, HP; Puthussery, D; G, DR; V, JC; Zhang, Y; Fang, Z; Lin, Z; Zhang, Y; Lin, C; Zhang, L; Mao, L; Wu, M; Vi Vo, TT; Kim, SH; Lee, T; Kondo, S; Kasai, S; Dumbhare, P; Phuse, V; Dubey, Y; Jamthikar, A; Le Vuong, TT; Kwak, JT; Ziaei, D; Jung, H; Miao, T; Snead, DRJ; Ahmed Raza, SE; Minhas, F; Rajpoot, NM;

Publication
CoRR

Abstract

2022

Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector

Authors
Costa, P; Fu, Y; Nunes, J; Campilho, A; Cardoso, JS;

Publication
CoRR

Abstract

2023

Lesion-Aware Chest Radiography Abnormality Classification with Object Detection Framework

Authors
Pedrosa, J; Sousa, P; Silva, J; Mendonça, AM; Campilho, A;

Publication
2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS

Abstract
Chest radiography is one of the most ubiquitous medical imaging modalities. Nevertheless, the interpretation of chest radiography images is time-consuming, complex and subject to observer variability. As such, automated diagnosis systems for pathology detection have been proposed, aiming to reduce the burden on radiologists. The advent of deep learning has fostered the development of solutions for both abnormality detection with promising results. However, these tools suffer from poor explainability as the reasons that led to a decision cannot be easily understood, representing a major hurdle for their adoption in clinical practice. In order to overcome this issue, a method for chest radiography abnormality detection is presented which relies on an object detection framework to detect individual findings and thus separate normal and abnormal CXRs. It is shown that this framework is capable of an excellent performance in abnormality detection (AUC: 0.993), outperforming other state-of-the-art classification methodologies (AUC: 0.976 using the same classes). Furthermore, validation on external datasets shows that the proposed framework has a smaller drop in performance when applied to previously unseen data (21.9% vs 23.4% on average). Several approaches for object detection are compared and it is shown that merging pathology classes to minimize radiologist variability improves the localization of abnormal regions (0.529 vs 0.491 APF when using all pathology classes), resulting in a network which is more explainable and thus more suitable for integration in clinical practice.

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