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Article
Open AccessFew-shot out-of-distribution detection for automated screening in retinal OCT images using deep learning
Deep neural networks have been increasingly proposed for automated screening and diagnosis of retinal diseases from optical coherence tomography (OCT), but often provide high-confidence predictions on out-of-d...
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Chapter and Conference Paper
Transformer-Based End-to-End Classification of Variable-Length Volumetric Data
The automatic classification of 3D medical data is memory-intensive. Also, variations in the number of slices between samples is common. Naïve solutions such as subsampling can solve these problems, but at the...
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Article
Retinal and choroidal vasoreactivity in central serous chorioretinopathy
This study aims to investigate retinal and choroidal vascular reactivity to carbogen in central serous chorioretinopathy (CSC) patients.
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Chapter and Conference Paper
Optic Disc and Fovea Detection in Color Eye Fundus Images
The optic disc (OD) and the fovea are relevant landmarks in fundus images. Their localization and segmentation can facilitate the detection of some retinal lesions and the assessment of their importance to the...
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Article
Open AccessiW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an a...
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Chapter and Conference Paper
A No-Reference Quality Metric for Retinal Vessel Tree Segmentation
Due to inevitable differences between the data used for training modern CAD systems and the data encountered when they are deployed in clinical scenarios, the ability to automatically assess the quality of pre...
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Chapter and Conference Paper
Retinal Image Quality Assessment by Mean-Subtracted Contrast-Normalized Coefficients
The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean...
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Chapter and Conference Paper
Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography
We propose a deep learning-based pipeline that, given a low-dose computed tomography of a patient chest, recommends if a patient should be submitted to further lung cancer assessment. The algorithm is composed...
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Chapter and Conference Paper
UOLO - Automatic Object Detection and Segmentation in Biomedical Images
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract repres...
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Chapter and Conference Paper
Improving Convolutional Neural Network Design via Variable Neighborhood Search
An unsupervised method for convolutional neural network (CNN) architecture design is proposed. The method relies on a variable neighborhood search-based approach for finding CNN architectures and hyperparamete...
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Chapter and Conference Paper
Optical Flow Based Approach for Automatic Cardiac Cycle Estimation in Ultrasound Images of the Carotid
This paper proposes a method to detect a reference frame in an ultrasound video of the carotid artery. This reference frame, usually located at the end of the diastole, is used as the location to measure sever...
