133 citations · 412 across the 16 of their papers we have counts for
17 papers
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
ExplainFix: Explainable Spatially Fixed Deep Networks
Alex Gaudio, Christos Faloutsos, Asim Smailagic +2
Is there an initialization for deep networks that requires no learning? ExplainFix adopts two design principles: the "fixed filters" principle that all spatial filter weights of co…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection
Elvin Johnson, Shreshta Mohan, Alex Gaudio +3
Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We fra…
Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector
Pedro Costa, Yongpan Fu, João Nunes +2
Cancer is one of the leading causes of death in the developed world. Cancer diagnosis is performed through the microscopic analysis of a sample of suspicious tissue. This process i…
Enhancement of Retinal Fundus Images via Pixel Color Amplification
Alex Gaudio, Asim Smailagic, Aurélio Campilho
We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distort…
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Asim Smailagic, Anupma Sharan, Pedro Costa +3
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world. The main aim of this paper is to improve the accuracy of Diabetic Retinopathy det…
LNDb: A Lung Nodule Database on Computed Tomography
João Pedrosa, Guilherme Aresta, Carlos Ferreira +8
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested…
Did you miss it? Automatic lung nodule detection combined with gaze information improves radiologists' screening performance
Guilherme Aresta, Carlos Ferreira, João Pedrosa +8
Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, search lung nodules is a h…
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
Teresa Araújo, Guilherme Aresta, Luís Mendonça +5
Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patients, but the screening process can be tiresome and prone to errors. Deep le…
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Asim Smailagic, Pedro Costa, Alex Gaudio +9
Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend…
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
Guilherme Aresta, Colin Jacobs, Teresa Araújo +4
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…
UOLO - automatic object detection and segmentation in biomedical images
Teresa Araújo, Guilherme Aresta, Adrian Galdran +3
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…
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis
Asim Smailagic, Hae Young Noh, Pedro Costa +6
Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance.Deep learning model…
Phenomenological Inclusion of Alternative Dispersion Relations to the Teukolsky Equation and its Application to Bounding the Graviton Mass with Gravitational-wave Measurements
Ka-Wai Chung, Tjonnie Guang Feng Li
Existing constraints on the graviton mass from gravitational-wave detections rely on the phase difference developed between different frequencies during the propagation. Effects on…
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis
Adrian Galdran, Aitor Alvarez-Gila, Maria Ines Meyer +7
Dermoscopic skin images are often obtained with different imaging devices, under varying acquisition conditions. In this work, instead of attempting to perform intensity and color…
Towards Adversarial Retinal Image Synthesis
Pedro Costa, Adrian Galdran, Maria Inês Meyer +4
Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be gene…