Publicaciones

Pablo Messina

2022, Publisher: CLEF2021 Working Notes, CEUR Workshop Proceedings, Link>

AUTHORS

Ria Deane, Pablo Pino, Pablo Messina, José Miguel Quintana, H Lobel, Denis Parra, Daniel Florea

ABSTRACT

This article describes the participation and results of the PUC Chile team in the Turberculosis task in the context of ImageCLEFmedical challenge 2021. We were ranked 7th based on the kappa metric and 4th in terms of accuracy. We describe three approaches we tried in order to address the task. Our best approach used 2D images visually encoded with a DenseNet neural network, which representations were concatenated to finally output the classification with a softmax layer. We describe in detail this and other two approaches, and we conclude by discussing some ideas for future work.


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RL1, Publisher: Working Notes of CLEF, Link>

AUTHORS

H Lobel, Ricardo Schilling, Pablo Messina, Denis Parra

ABSTRACT

This paper describes the submission of the IALab group of the Pontifical Catholic University of Chile to the Medical Domain Visual Question Answering (VQA-Med) task. Our participation was rather simple: we approached the problem as image classification. We took a DenseNet121 with its weights pre-trained in ImageNet and fine-tuned it with the VQA-Med 2020 dataset labels to predict the answer. Different answers were treated as different classes, and the questions were disregarded for simplicity since essentially they all ask for abnormalities. With this very simple approach we ranked 7th among 11 teams, with a test set accuracy of 0.236.


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2022, Publisher: ACM Computing Surveys, Link>

AUTHORS

Sergio Uribe, Pablo Pino, Pablo Messina, Marcelo Andia, Denis Parra, Daniel Capurro, Cristian Tejos, Claudia Prieto, Cecilia Besa, Álvaro Soto

ABSTRACT

Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.


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