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Author Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval Type Conference Article
  Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 4022-4032  
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  Abstract (up)  
  Address Virtual; January 2021  
  Corporate Author Thesis  
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  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ MDB2021 Serial 3491  
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Author Andres Mafla; Rafael S. Rezende; Lluis Gomez; Diana Larlus; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title StacMR: Scene-Text Aware Cross-Modal Retrieval Type Conference Article
  Year 2021 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 2219-2229  
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  Abstract (up)  
  Address Virtual; January 2021  
  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 WACV  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ MRG2021a Serial 3492  
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Author Sonia Baeza; R.Domingo; M.Salcedo; G.Moragas; J.Deportos; I.Garcia Olive; Carles Sanchez; Debora Gil; Antoni Rosell edit  url
openurl 
  Title Artificial Intelligence to Optimize Pulmonary Embolism Diagnosis During Covid-19 Pandemic by Perfusion SPECT/CT, a Pilot Study Type Journal Article
  Year 2021 Publication American Journal of Respiratory and Critical Care Medicine Abbreviated Journal  
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  Area Expedition Conference  
  Notes IAM; 600.145 Approved no  
  Call Number Admin @ si @ BDS2021 Serial 3591  
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Author Josep Llados edit  openurl
  Title The 5G of Document Intelligence Type Conference Article
  Year 2021 Publication 3rd Workshop on Future of Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract (up)  
  Address Lausanne; Suissa; September 2021  
  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 FDAR  
  Notes DAG Approved no  
  Call Number Admin @ si @ Serial 3677  
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Author AN Ruchai; VI Kober; KA Dorofeev; VN Karnaukhov; Mikhail Mozerov edit  url
doi  openurl
  Title Classification of breast abnormalities using a deep convolutional neural network and transfer learning Type Journal Article
  Year 2021 Publication Journal of Communications Technology and Electronics Abbreviated Journal  
  Volume 66 Issue 6 Pages 778–783  
  Keywords  
  Abstract (up) A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ RKD2022 Serial 3680  
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Author Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown edit   pdf
openurl 
  Title Noise Prism: A Novel Multispectral Visualization Technique Type Journal Article
  Year 2021 Publication 31st Color and Imaging Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract (up) A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique.
 
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  Area Expedition Conference CIC  
  Notes MACO; CIC Approved no  
  Call Number Admin @ si @ CVL2021 Serial 4000  
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Author Shun Yao; Fei Yang; Yongmei Cheng; Mikhail Mozerov edit   pdf
url  doi
openurl 
  Title 3D Shapes Local Geometry Codes Learning with SDF Type Conference Article
  Year 2021 Publication International Conference on Computer Vision Workshops Abbreviated Journal  
  Volume Issue Pages 2110-2117  
  Keywords  
  Abstract (up) A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics.  
  Address VIRTUAL; October 2021  
  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 ICCVW  
  Notes LAMP Approved no  
  Call Number Admin @ si @ YYC2021 Serial 3681  
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez edit  doi
openurl 
  Title Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Book Chapter
  Year 2021 Publication Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 Abbreviated Journal  
  Volume 15 Issue Pages 89–93  
  Keywords  
  Abstract (up) Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces.  
  Address  
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  Publisher Springer Nature Place of Publication Editor  
  Language Summary Language Original Title  
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  Notes IAM; DAG; 600.120; 600.145; 600.139 Approved no  
  Call Number Admin @ si @ GRP2021 Serial 3594  
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Author Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer edit  url
openurl 
  Title When Deep Learners Change Their Mind: Learning Dynamics for Active Learning Type Conference Article
  Year 2021 Publication 19th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 13052 Issue 1 Pages 403-413  
  Keywords  
  Abstract (up) Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.  
  Address September 2021  
  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 CAIP  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ ZRV2021 Serial 3673  
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Author Javad Zolfaghari Bengar; Joost Van de Weijer; Bartlomiej Twardowski; Bogdan Raducanu edit  url
doi  openurl
  Title Reducing Label Effort: Self- Supervised Meets Active Learning Type Conference Article
  Year 2021 Publication International Conference on Computer Vision Workshops Abbreviated Journal  
  Volume Issue Pages 1631-1639  
  Keywords  
  Abstract (up) Active learning is a paradigm aimed at reducing the annotation effort by training the model on actively selected informative and/or representative samples. Another paradigm to reduce the annotation effort is self-training that learns from a large amount of unlabeled data in an unsupervised way and fine-tunes on few labeled samples. Recent developments in self-training have achieved very impressive results rivaling supervised learning on some datasets. The current work focuses on whether the two paradigms can benefit from each other. We studied object recognition datasets including CIFAR10, CIFAR100 and Tiny ImageNet with several labeling budgets for the evaluations. Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high. The performance gap between active learning trained either with self-training or from scratch diminishes as we approach to the point where almost half of the dataset is labeled.  
  Address October 2021  
  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 ICCVW  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ ZVT2021 Serial 3672  
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Author Carola Figueroa Flores; Bogdan Raducanu; David Berga; Joost Van de Weijer edit   pdf
openurl 
  Title Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains Type Conference Article
  Year 2021 Publication 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal  
  Volume 4 Issue Pages 163-171  
  Keywords  
  Abstract (up) arXiv:2007.12562
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification. In the current paper, we propose an approach which does not require explicit saliency maps to improve image classification, but they are learned implicitely, during the training of an end-to-end image classification task. We show that our approach obtains similar results as the case when the saliency maps are provided explicitely. Combining RGB data with saliency maps represents a significant advantage for object recognition, especially for the case when training data is limited. We validate our method on several datasets for fine-grained classification tasks (Flowers, Birds and Cars). In addition, we show that our saliency estimation method, which is trained without any saliency groundtruth data, obtains competitive results on real image saliency benchmark (Toronto), and outperforms deep saliency models with synthetic images (SID4VAM).
 
  Address Virtual; February 2021  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference VISAPP  
  Notes LAMP Approved no  
  Call Number Admin @ si @ FRB2021c Serial 3540  
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Author Fatemeh Noroozi; Ciprian Corneanu; Dorota Kamińska; Tomasz Sapiński; Sergio Escalera; Gholamreza Anbarjafari edit   pdf
url  openurl
  Title Survey on Emotional Body Gesture Recognition Type Journal Article
  Year 2021 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume 12 Issue 2 Pages 505 - 523  
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  Abstract (up) Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as “body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ NCK2021 Serial 3657  
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Author O.F.Ahmad; Y.Mori; M.Misawa; S.Kudo; J.T.Anderson; Jorge Bernal edit  url
doi  openurl
  Title Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method Type Journal Article
  Year 2021 Publication Endoscopy Abbreviated Journal END  
  Volume 53 Issue 9 Pages 893-901  
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  Abstract (up) BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.  
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  Notes ISE Approved no  
  Call Number Admin @ si @ AMM2021 Serial 3670  
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Author Giuseppe Pezzano; Vicent Ribas Ripoll; Petia Radeva edit  url
openurl 
  Title CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation Type Journal Article
  Year 2021 Publication Computer Methods and Programs in Biomedicine Abbreviated Journal CMPB  
  Volume 198 Issue Pages 105792  
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  Abstract (up) Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of and respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software.  
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ PRR2021 Serial 3530  
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Author Marta Ligero; Alonso Garcia Ruiz; Cristina Viaplana; Guillermo Villacampa; Maria V Raciti; Jaid Landa; Ignacio Matos; Juan Martin Liberal; Maria Ochoa de Olza; Cinta Hierro; Joaquin Mateo; Macarena Gonzalez; Rafael Morales Barrera; Cristina Suarez; Jordi Rodon; Elena Elez; Irene Braña; Eva Muñoz-Couselo; Ana Oaknin; Roberta Fasani; Paolo Nuciforo; Debora Gil; Carlota Rubio Perez; Joan Seoane; Enriqueta Felip; Manuel Escobar; Josep Tabernero; Joan Carles; Rodrigo Dienstmann; Elena Garralda; Raquel Perez Lopez edit  url
doi  openurl
  Title A CT-based radiomics signature is associated with response to immune checkpoint inhibitors in advanced solid tumors Type Journal Article
  Year 2021 Publication Radiology Abbreviated Journal  
  Volume 299 Issue 1 Pages 109-119  
  Keywords  
  Abstract (up) Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue.  
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  Notes IAM; 600.145 Approved no  
  Call Number Admin @ si @ LGV2021 Serial 3593  
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