C-BER - Indexed Articles in Journals

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Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images

( 2020) Joana Maria Rocha ; António Cunha ; Ana Maria Mendonça ; 7800 ; 6271 ; 6381
Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

( 2021) Joana Maria Rocha ; Ana Maria Mendonça ; Aurélio Campilho ; 6071 ; 6381 ; 7800

Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.