activity
20162025
most citedDomain-adversarial neural networks to address the appearance variability of histopathology images

1.1k citations · 1.5k across the 13 of their papers we have counts for

collaborators

19 papers

cs.CV2025

SurfR: Surface Reconstruction with Multi-scale Attention

Siddhant Ranade, Gonçalo Dias Pais, Ross Tyler Whitaker +3

We propose a fast and accurate surface reconstruction algorithm for unorganized point clouds using an implicit representation. Recent learning methods are either single-object repr…

cs.CV2024

Correspondence Free Multivector Cloud Registration using Conformal Geometric Algebra

Francisco Xavier Vasconcelos, Jacinto C. Nascimento

We present, for the first time, a novel theoretical approach to address the problem of correspondence free multivector cloud registration in conformal geometric algebra. Such forma…

cs.CV2024

Latent Embedding Clustering for Occlusion Robust Head Pose Estimation

José Celestino, Manuel Marques, Jacinto C. Nascimento

Head pose estimation has become a crucial area of research in computer vision given its usefulness in a wide range of applications, including robotics, surveillance, or driver atte…

cs.CV2023★ 19 cited

2D Image head pose estimation via latent space regression under occlusion settings

José Celestino, Manuel Marques, Jacinto C. Nascimento +1

Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems stil…

eess.IV2023★ 2 cited

MDF-Net for abnormality detection by fusing X-rays with clinical data

Chihcheng Hsieh, Isabel Blanco Nobre, Sandra Costa Sousa +5

This study investigates the effects of including patients' clinical information on the performance of deep learning (DL) classifiers for disease location in chest X-ray images. Alt…

cs.CV2022★ 1 cited

Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images

Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1

Survival time prediction from medical images is important for treatment planning, where accurate estimations can improve healthcare quality. One issue affecting the training of sur…

cs.CV2021

Post-hoc Overall Survival Time Prediction from Brain MRI

Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1

Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOT…

cs.HC2020★ 36 cited

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

Francisco Maria Calisto, Nuno Jardim Nunes, Jacinto Carlos Nascimento

This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described her…

cs.CV2020

Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification

Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1

The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classific…

cs.CV2019

Unsupervised Task Design to Meta-Train Medical Image Classifiers

Gabriel Maicas, Cuong Nguyen, Farbod Motlagh +2

Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with smal…

cs.CV2019

3DRegNet: A Deep Neural Network for 3D Point Registration

G. Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu +3

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the fol…

cs.CV2019

OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras

G. Dias Pais, Tiago J. Dias, Jacinto C. Nascimento +1

Pedestrian detection is one of the most explored topics in computer vision and robotics. The use of deep learning methods allowed the development of new and highly competitive algo…

cs.CV2018★ 283 cited

Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

Marc Combalia, Veronica Vilaplana

We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…

cs.CV2018

Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI

Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento +2

We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., labels are available only at the im…

cs.CV2018

Training Medical Image Analysis Systems like Radiologists

Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento +2

The training of medical image analysis systems using machine learning approaches follows a common script: collect and annotate a large dataset, train the classifier on the training…

cs.CV2017★ 1.1k cited

Domain-adversarial neural networks to address the appearance variability of histopathology images

Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof +2

Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab…

cs.RO2016

Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation

Andre Mateus, David Ribeiro, Pedro Miraldo +1

This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this pape…

cs.RO2016

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

David Ribeiro, Andre Mateus, Pedro Miraldo +1

A real-time Deep Learning based method for Pedestrian Detection (PD) is applied to the Human-Aware robot navigation problem. The pedestrian detector combines the Aggregate Channel…

cs.CV2016★ 1 cited

Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography

Gustavo Carneiro, Luke Oakden-Rayner, Andrew P. Bradley +2

We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this pr…