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Author Carlo Gatta; Eloi Puertas; Oriol Pujol
Title (down) Multi-Scale Stacked Sequential Learning Type Journal Article
Year 2011 Publication Pattern Recognition Abbreviated Journal PR
Volume 44 Issue 10-11 Pages 2414-2416
Keywords Stacked sequential learning; Multiscale; Multiresolution; Contextual classification
Abstract One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ GPP2011 Serial 1802
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Author Xinhang Song; Shuqiang Jiang; Luis Herranz
Title (down) Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold Type Journal Article
Year 2017 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 26 Issue 6 Pages 2721-2735
Keywords
Abstract Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ SJH2017a Serial 2963
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Author Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund
Title (down) Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification Type Journal Article
Year 2022 Publication Automation in Construction Abbreviated Journal AC
Volume 144 Issue Pages 104614
Keywords Sewer Defect Classification; Vision Transformers; Sinkhorn-Knopp; Convolutional Neural Networks; Closed-Circuit Television; Sewer Inspection
Abstract A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model spatial semantics in the images, features are aggregated at different scales non-locally through the use of a lightweight vision transformer, and a smaller set of tokens was produced through a novel Sinkhorn clustering-based tokenizer using distinct cluster centers. The proposed MSHViT and Sinkhorn tokenizer were evaluated on the Sewer-ML multi-label sewer defect classification dataset, showing consistent performance improvements of up to 2.53 percentage points.
Address Dec 2022
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA Approved no
Call Number Admin @ si @ BME2022c Serial 3780
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Author Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
Title (down) Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization Type Journal Article
Year 2021 Publication International Journal of Imaging Systems and Technology Abbreviated Journal IMA
Volume 31 Issue 4 Pages 2170-2188
Keywords
Abstract Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ DGR2021a Serial 3630
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Author Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras
Title (down) Multi-part body segmentation based on depth maps for soft biometry analysis Type Journal Article
Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 56 Issue Pages 14-21
Keywords 3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis
Abstract This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB Approved no
Call Number Admin @ si @ MEG2015 Serial 2588
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Author Partha Pratim Roy; Umapada Pal; Josep Llados; Mathieu Nicolas Delalandre
Title (down) Multi-oriented touching text character segmentation in graphical documents using dynamic programming Type Journal Article
Year 2012 Publication Pattern Recognition Abbreviated Journal PR
Volume 45 Issue 5 Pages 1972-1983
Keywords
Abstract 2,292 JCR
The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-3203 ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ RPL2012a Serial 2133
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Author Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal
Title (down) Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing Type Journal Article
Year 2014 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 72 Issue 1 Pages 515-539
Keywords
Abstract In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1380-7501 ISBN Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ SDT2014 Serial 2357
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Author Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados
Title (down) Multi-oriented Bangla and Devnagari text recognition Type Journal Article
Year 2010 Publication Pattern Recognition Abbreviated Journal PR
Volume 43 Issue 12 Pages 4124–4136
Keywords
Abstract There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.

For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ PRT2010 Serial 1337
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Author Albert Clapes; Miguel Reyes; Sergio Escalera
Title (down) Multi-modal User Identification and Object Recognition Surveillance System Type Journal Article
Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 34 Issue 7 Pages 799-808
Keywords Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning
Abstract We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; 600.046; 605.203;MILAB Approved no
Call Number Admin @ si @ CRE2013 Serial 2248
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Author Cristina Palmero; Albert Clapes; Chris Bahnsen; Andreas Møgelmose; Thomas B. Moeslund; Sergio Escalera
Title (down) Multi-modal RGB-Depth-Thermal Human Body Segmentation Type Journal Article
Year 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 118 Issue 2 Pages 217-239
Keywords Human body segmentation; RGB ; Depth Thermal
Abstract This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB; Approved no
Call Number Admin @ si @ PCB2016 Serial 2767
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title (down) Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine Type Journal Article
Year 2018 Publication Entropy Abbreviated Journal ENTROPY
Volume 20 Issue 11 Pages 809
Keywords hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image
Abstract In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKE2018 Serial 3198
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Author Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou
Title (down) Multi-Illuminant Estimation with Conditional Random Fields Type Journal Article
Year 2014 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 23 Issue 1 Pages 83-95
Keywords color constancy; CRF; multi-illuminant
Abstract Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1057-7149 ISBN Medium
Area Expedition Conference
Notes CIC; LAMP; 600.074; 600.079 Approved no
Call Number Admin @ si @ BRW2014 Serial 2451
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
Title (down) Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams Type Journal Article
Year 2016 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 149 Issue Pages 146-156
Keywords
Abstract Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; Approved no
Call Number Admin @ si @ ADR2016b Serial 2742
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Author Juan Jose Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger
Title (down) Multi-class structural damage segmentation using fully convolutional networks Type Journal Article
Year 2019 Publication Computers in Industry Abbreviated Journal COMPUTIND
Volume 112 Issue Pages 103121
Keywords Bridge damage detection; Deep learning; Semantic segmentation
Abstract Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ RKL2019 Serial 3315
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Author Victor M. Campello; Polyxeni Gkontra; Cristian Izquierdo; Carlos Martin-Isla; Alireza Sojoudi; Peter M. Full; Klaus Maier-Hein; Yao Zhang; Zhiqiang He; Jun Ma; Mario Parreno; Alberto Albiol; Fanwei Kong; Shawn C. Shadden; Jorge Corral Acero; Vaanathi Sundaresan; Mina Saber; Mustafa Elattar; Hongwei Li; Bjoern Menze; Firas Khader; Christoph Haarburger; Cian M. Scannell; Mitko Veta; Adam Carscadden; Kumaradevan Punithakumar; Xiao Liu; Sotirios A. Tsaftaris; Xiaoqiong Huang; Xin Yang; Lei Li; Xiahai Zhuang; David Vilades; Martin L. Descalzo; Andrea Guala; Lucia La Mura; Matthias G. Friedrich; Ria Garg; Julie Lebel; Filipe Henriques; Mahir Karakas; Ersin Cavus; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Jose F. Rodriguez Palomares; Karim Lekadir
Title (down) Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge Type Journal Article
Year 2021 Publication IEEE Transactions on Medical Imaging Abbreviated Journal TMI
Volume 40 Issue 12 Pages 3543-3554
Keywords
Abstract The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ CGI2021 Serial 3653
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