Search Results for author: Jacinto C. Nascimento

Found 11 papers, 3 papers with code

Post-hoc Overall Survival Time Prediction from Brain MRI

1 code implementation22 Feb 2021 Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro

In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training.

Brain Tumor Segmentation Tumor Segmentation

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

1 code implementation21 May 2020 Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento, Gustavo Carneiro

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 classification decision.

Classification General Classification +2

Unsupervised Task Design to Meta-Train Medical Image Classifiers

no code implementations17 Jul 2019 Gabriel Maicas, Cuong Nguyen, Farbod Motlagh, Jacinto C. Nascimento, Gustavo Carneiro

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 small training sets).

Classification Few-Shot Learning +1

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

1 code implementation CVPR 2020 G. Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C. Nascimento, Rama Chellappa, Pedro Miraldo

Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame.

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

no code implementations25 Sep 2018 Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro

Conversely, traditional approaches follow a pre-hoc approach that initially localises suspicious areas that are subsequently classified to establish the breast malignancy -- this approach is trained using strongly annotated data (i. e., it needs a delineation and classification of all lesions in an image).

Training Medical Image Analysis Systems like Radiologists

no code implementations28 May 2018 Gabriel Maicas, Andrew P. Bradley, Jacinto C. Nascimento, Ian Reid, Gustavo Carneiro

This process bears no direct resemblance with radiologist training, which is based on solving a series of tasks of increasing difficulty, where each task involves the use of significantly smaller datasets than those used in machine learning.

Classification Curriculum Learning +3

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

no code implementations15 Jul 2016 David Ribeiro, Andre Mateus, Pedro Miraldo, Jacinto C. Nascimento

A real-time Deep Learning based method for Pedestrian Detection (PD) is applied to the Human-Aware robot navigation problem.

Pedestrian Detection Robot Navigation

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

no code implementations15 Jul 2016 Andre Mateus, David Ribeiro, Pedro Miraldo, Jacinto C. Nascimento

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

Human Detection Pedestrian Detection

Non-rigid Segmentation using Sparse Low Dimensional Manifolds and Deep Belief Networks

no code implementations CVPR 2014 Jacinto C. Nascimento, Gustavo Carneiro

In this paper, we propose a new methodology for segmenting non-rigid visual objects, where the search procedure is onducted directly on a sparse low-dimensional manifold, guided by the classification results computed from a deep belief network.

Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds

no code implementations CVPR 2013 Jacinto C. Nascimento, Gustavo Carneiro

In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection search space of current stateof-the-art top-down segmentation methodologies.

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