C-BER - Indexed Articles in Journals

Permanent URI for this collection

http://localhost:4000/handle/123456789/330

Browse

Browsing C-BER - Indexed Articles in Journals by Author "6071"
  • Item
    End-to-end Adversarial Retinal Image Synthesis
    ( 2017) Costa,P ; Adrian Galdran ; Maria Inês Meyer ; Niemeijer,M ; Abramoff,M ; Ana Maria Mendonça ; Aurélio Campilho ; 6071 ; 6381 ; 6825 ; 6835
    In medical image analysis applications, the availability of large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a Generative Adversarial Network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality. IEEE
  • Item
    Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images
    ( 2018) Teresa Finisterra Araújo ; Ana Maria Mendonça ; Aurélio Campilho ; 6320 ; 6381 ; 6071
  • Item
    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.
  • Item
    A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images
    ( 2018) Costa,P ; Adrian Galdran ; Smailagic,A ; Aurélio Campilho ; 6071 ; 6825
    Diabetic retinopathy (DR) detection is a critical retinal image analysis task in the context of early blindness prevention. Unfortunately, in order to train a model to accurately detect DR based on the presence of different retinal lesions, typically a dataset with medical expert's annotations at the pixel level is needed. In this paper, a new methodology based on the multiple instance learning (MIL) framework is developed in order to overcome this necessity by leveraging the implicit information present on annotations made at the image level. Contrary to previous MIL-based DR detection systems, the main contribution of the proposed technique is the joint optimization of the instance encoding and the image classification stages. In this way, more useful mid-level representations of pathological images can be obtained. The explainability of the model decisions is further enhanced by means of a new loss function enforcing appropriate instance and mid-level representations. The proposed technique achieves comparable or better results than other recently proposed methods, with 90% area under the receiver operating characteristic curve (AUC) on Messidor, 93% AUC on DR1, and 96% AUC on DR2, while improving the interpretability of the produced decisions.