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Found 15 papers, 6 papers with code
3DRegNet: A Deep Neural Network for 3D Point Registration
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.
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
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.
MDF-Net for abnormality detection by fusing X-rays with clinical data
In this work, we propose a novel architecture consisting of two fusion methods that enable the model to simultaneously process patients' clinical data (structured data) and chest X-rays (image data).
2D Image head pose estimation via latent space regression under occlusion settings
Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications.
Ranked #4 on
Head Pose Estimation
on BIWI
Post-hoc Overall Survival Time Prediction from Brain MRI
In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training.
Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images
In this work, we propose a new training method that predicts survival time using all censored and uncensored data.
Training Medical Image Analysis Systems like Radiologists
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.
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation
This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system.
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation
A real-time Deep Learning based method for Pedestrian Detection (PD) is applied to the Human-Aware robot navigation problem.
Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI
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).
Top-Down Segmentation of Non-rigid Visual Objects Using Derivative-Based Search on Sparse Manifolds
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.
Non-rigid Segmentation using Sparse Low Dimensional Manifolds and Deep Belief Networks
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.
OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras
Pedestrian detection is one of the most explored topics in computer vision and robotics.
Unsupervised Task Design to Meta-Train Medical Image Classifiers
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).
Latent Embedding Clustering for Occlusion Robust Head Pose Estimation
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 attention monitoring.
