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Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
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A computational framework for cancer response assessment based on oncological PET-CT scans |
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2014 |
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Computers in Biology and Medicine |
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CBM |
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55 |
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92–99 |
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Computer aided diagnosis; Nuclear medicine; Machine learning; Image processing; Quantitative analysis |
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In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks. |
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HuPBA;MILAB |
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no |
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Admin @ si @ SED2014 |
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2606 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Puig |
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Title |
Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation |
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2014 |
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Pattern Recognition Letters |
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PRL |
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46 |
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1-10 |
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Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation |
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In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. |
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HuPBA;MILAB |
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no |
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Admin @ si @ SEP2014 |
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2550 |
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Author |
Gerard Canal; Sergio Escalera; Cecilio Angulo |
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A Real-time Human-Robot Interaction system based on gestures for assistive scenarios |
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2016 |
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Computer Vision and Image Understanding |
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CVIU |
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149 |
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65-77 |
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Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation |
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Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. |
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Elsevier B.V. |
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HuPBA;MILAB; |
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no |
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Admin @ si @ CEA2016 |
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2768 |
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Author |
Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Chenxu Zhao; Xu Zhang; Stan Z Li; Zhen Lei |
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Title |
Surveillance Face Anti-spoofing |
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Year |
2024 |
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IEEE Transactions on Information Forensics and Security |
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TIFS |
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19 |
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1535-1546 |
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Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL. |
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HUPBA |
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no |
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Admin @ si @ FLW2024 |
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3869 |
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Author |
Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund |
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Title |
Back-dropout Transfer Learning for Action Recognition |
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Journal Article |
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2018 |
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IET Computer Vision |
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IETCV |
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12 |
Issue |
4 |
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484-491 |
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Learning (artificial intelligence); Pattern Recognition |
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Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate. |
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HUPBA; no proj |
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Admin @ si @ RKM2018 |
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3071 |
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