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Author Mohammad Momeny; Ali Asghar Neshat; Ahmad Jahanbakhshi; Majid Mahmoudi; Yiannis Ampatzidis; Petia Radeva
Title Grading and fraud detection of saffron via learning-to-augment incorporated Inception-v4 CNN Type Journal Article
Year 2023 Publication Food Control Abbreviated Journal (down) FC
Volume 147 Issue Pages 109554
Keywords
Abstract Saffron is a well-known product in the food industry. It is one of the spices that are sometimes adulterated with the sole motive of gaining more economic profit. Today, machine vision systems are widely used in controlling the quality of food and agricultural products as a new, non-destructive, and inexpensive approach. In this study, a machine vision system based on deep learning was used to detect fraud and saffron quality. A dataset of 1869 images was created and categorized in 6 classes including: dried saffron stigma using a dryer; dried saffron stigma using pressing method; pure stem of saffron; sunflower; saffron stem mixed with food coloring; and corn silk mixed with food coloring. A Learning-to-Augment incorporated Inception-v4 Convolutional Neural Network (LAII-v4 CNN) was developed for grading and fraud detection of saffron in images captured by smartphones. The best policies of data augmentation were selected with the proposed LAII-v4 CNN using images corrupted by Gaussian, speckle, and impulse noise to address overfitting the model. The proposed LAII-v4 CNN compared with regular CNN-based methods and traditional classifiers. Ensemble of Bagged Decision Trees, Ensemble of Boosted Decision Trees, k-Nearest Neighbor, Random Under-sampling Boosted Trees, and Support Vector Machine were used for classification of the features extracted by Histograms of Oriented Gradients and Local Binary Patterns, and selected by the Principal Component Analysis. The results showed that the proposed LAII-v4 CNN with an accuracy of 99.5% has achieved the best performance by employing batch normalization, Dropout, and leaky ReLU.
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 @ MNJ2023 Serial 3882
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Author Wenwen Fu; Zhihong An; Wendong Huang; Haoran Sun; Wenjuan Gong; Jordi Gonzalez
Title A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection Type Journal Article
Year 2023 Publication Electronics Abbreviated Journal (down) ELEC
Volume 12 Issue 18 Pages 3947
Keywords micro-expression spotting; sliding window; key frame extraction
Abstract Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME)2 database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58% for the CAS(ME)2 and 23.98% for the SAMM Long Videos according to overall F-scores.
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 ISE Approved no
Call Number Admin @ si @ FAH2023 Serial 3864
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Author Shiqi Yang; Yaxing Wang; Luis Herranz; Shangling Jui; Joost Van de Weijer
Title Casting a BAIT for offline and online source-free domain adaptation Type Journal Article
Year 2023 Publication Computer Vision and Image Understanding Abbreviated Journal (down) CVIU
Volume 234 Issue Pages 103747
Keywords
Abstract We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline setting where all target data can be visited multiple times (epochs) to arrive at a prediction for each target sample, and the online setting where the target data needs to be directly classified upon arrival. Inspired by diverse classifier based domain adaptation methods, in this paper we introduce a second classifier, but with another classifier head fixed. When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features. Next, when updating the feature extractor, those features will be pushed towards the right side of the source decision boundary, thus achieving source-free domain adaptation. Experimental results show that the proposed method achieves competitive results for offline SFDA on several benchmark datasets compared with existing DA and SFDA methods, and our method surpasses by a large margin other SFDA methods under online source-free domain adaptation setting.
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; MACO Approved no
Call Number Admin @ si @ YWH2023 Serial 3874
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Author Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil
Title BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation Type Journal Article
Year 2023 Publication Computer Methods and Programs in Biomedicine Abbreviated Journal (down) CMPB
Volume 228 Issue Pages 107241
Keywords Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation
Abstract Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy.
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 IAM; Approved no
Call Number Admin @ si @ BSC2023 Serial 3702
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Author P. Canals; Simone Balocco; O. Diaz; J. Li; A. Garcia Tornel; M. Olive Gadea; M. Ribo
Title A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning Type Journal Article
Year 2023 Publication Computerized Medical Imaging and Graphics Abbreviated Journal (down) CMIG
Volume 104 Issue 102170 Pages
Keywords Artificial intelligence; Deep learning; Stroke; Thrombectomy; Vascular feature extraction; Vascular tortuosity
Abstract Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.
Address
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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 MILAB Approved no
Call Number Admin @ si @ CBD2023 Serial 4005
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Author Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown
Title Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring Type Journal Article
Year 2023 Publication Computer Graphics Forum Abbreviated Journal (down) CGF
Volume Issue Pages
Keywords
Abstract Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework.
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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 CIC; MACO Approved no
Call Number Admin @ si @ XVH2023 Serial 3883
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Author Anders Skaarup Johansen; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund
Title Who Cares about the Weather? Inferring Weather Conditions for Weather-Aware Object Detection in Thermal Images Type Journal Article
Year 2023 Publication Applied Sciences Abbreviated Journal (down) AS
Volume 13 Issue 18 Pages
Keywords thermal; object detection; concept drift; conditioning; weather recognition
Abstract Deployments of real-world object detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, two types of weather-aware latent conditioning methods are investigated. The proposed method aims to guide two object detectors, (YOLOv5 and Deformable DETR) to become weather-aware. This is achieved by leveraging an auxiliary branch that predicts weather-related information while conditioning intermediate layers of the object detector. While the conditioning methods proposed do not directly improve the accuracy of baseline detectors, it can be observed that conditioned networks manage to extract a weather-related signal from the thermal images, thus resulting in a decreased miss rate at the cost of increased false positives. The extracted signal appears noisy and is thus challenging to regress accurately. This is most likely a result of the qualitative nature of the thermal sensor; thus, further work is needed to identify an ideal method for optimizing the conditioning branch, as well as to further improve the accuracy of the system.
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
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Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ SNE2023 Serial 3983
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Author Cristhian A. Aguilera-Carrasco; Luis Felipe Gonzalez-Böhme; Francisco Valdes; Francisco Javier Quitral Zapata; Bogdan Raducanu
Title A Hand-Drawn Language for Human–Robot Collaboration in Wood Stereotomy Type Journal Article
Year 2023 Publication IEEE Access Abbreviated Journal (down) ACCESS
Volume 11 Issue Pages 100975 - 100985
Keywords
Abstract This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes LAMP Approved no
Call Number Admin @ si @ AGV2023 Serial 3969
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Author Armin Mehri; Parichehr Behjati; Angel Sappa
Title TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution Type Journal Article
Year 2023 Publication IEEE Access Abbreviated Journal (down) ACCESS
Volume 11 Issue Pages 11529-11540
Keywords
Abstract Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient.
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 MSIAU Approved no
Call Number Admin @ si @ MBS2023 Serial 3876
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Author Armin Mehri; Parichehr Behjati; Dario Carpio; Angel Sappa
Title SRFormer: Efficient Yet Powerful Transformer Network for Single Image Super Resolution Type Journal Article
Year 2023 Publication IEEE Access Abbreviated Journal (down) ACCESS
Volume 11 Issue Pages
Keywords
Abstract Recent breakthroughs in single image super resolution have investigated the potential of deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs based models suffer from their limited fields and their inability to adapt to the input content. Recently, Transformer based models were presented, which demonstrated major performance gains in Natural Language Processing and Vision tasks while mitigating the drawbacks of CNNs. Nevertheless, Transformer computational complexity can increase quadratically for high-resolution images, and the fact that it ignores the original structures of the image by converting them to the 1D structure can make it problematic to capture the local context information and adapt it for real-time applications. In this paper, we present, SRFormer, an efficient yet powerful Transformer-based architecture, by making several key designs in the building of Transformer blocks and Transformer layers that allow us to consider the original structure of the image (i.e., 2D structure) while capturing both local and global dependencies without raising computational demands or memory consumption. We also present a Gated Multi-Layer Perceptron (MLP) Feature Fusion module to aggregate the features of different stages of Transformer blocks by focusing on inter-spatial relationships while adding minor computational costs to the network. We have conducted extensive experiments on several super-resolution benchmark datasets to evaluate our approach. SRFormer demonstrates superior performance compared to state-of-the-art methods from both Transformer and Convolutional networks, with an improvement margin of 0.1∼0.53dB . Furthermore, while SRFormer has almost the same model size, it outperforms SwinIR by 0.47% and inference time by half the time of SwinIR. The code will be available on GitHub.
Address
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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 MSIAU Approved no
Call Number Admin @ si @ MBC2023 Serial 3887
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Author Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen
Title Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images Type Miscellaneous
Year 2023 Publication Arxiv Abbreviated Journal (down)
Volume Issue Pages 107241
Keywords
Abstract This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.
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
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Area Expedition Conference
Notes IAM Approved no
Call Number Admin @ si @ CCG2023 Serial 3855
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Author Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados
Title I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation Type Conference Article
Year 2023 Publication 21st International Graphonomics Conference Abbreviated Journal (down)
Volume Issue Pages 136–148
Keywords
Abstract During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.
Address Evora; Portugal; October 2023
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IGS
Notes DAG Approved no
Call Number Admin @ si @ BPG2023 Serial 3838
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Author Simone Zini; Alex Gomez-Villa; Marco Buzzelli; Bartlomiej Twardowski; Andrew D. Bagdanov; Joost Van de Weijer
Title Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training Type Conference Article
Year 2023 Publication 11th International Conference on Learning Representations Abbreviated Journal (down)
Volume Issue Pages
Keywords
Abstract Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation – which we call Planckian Jitter – that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information. Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets. In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations.
Address 1 -5 May 2023, Kigali, Ruanda
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 ICLR
Notes LAMP; 600.147; 611.008; 5300006 Approved no
Call Number Admin @ si @ ZGB2023 Serial 3820
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Author German Barquero; Sergio Escalera; Cristina Palmero
Title BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction Type Conference Article
Year 2023 Publication IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal (down)
Volume Issue Pages 2317-2327
Keywords
Abstract Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behavior
coupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area of
the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP.
Address 2-6 October 2023. Paris (France)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ICCV
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ BEP2023 Serial 3829
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Author Yael Tudela; Ana Garcia Rodriguez; Gloria Fernandez Esparrach; Jorge Bernal
Title Towards Fine-Grained Polyp Segmentation and Classification Type Conference Article
Year 2023 Publication Workshop on Clinical Image-Based Procedures Abbreviated Journal (down)
Volume 14242 Issue Pages 32-42
Keywords Medical image segmentation; Colorectal Cancer; Vision Transformer; Classification
Abstract Colorectal cancer is one of the main causes of cancer death worldwide. Colonoscopy is the gold standard screening tool as it allows lesion detection and removal during the same procedure. During the last decades, several efforts have been made to develop CAD systems to assist clinicians in lesion detection and classification. Regarding the latter, and in order to be used in the exploration room as part of resect and discard or leave-in-situ strategies, these systems must identify correctly all different lesion types. This is a challenging task, as the data used to train these systems presents great inter-class similarity, high class imbalance, and low representation of clinically relevant histology classes such as serrated sessile adenomas.

In this paper, a new polyp segmentation and classification method, Swin-Expand, is introduced. Based on Swin-Transformer, it uses a simple and lightweight decoder. The performance of this method has been assessed on a novel dataset, comprising 1126 high-definition images representing the three main histological classes. Results show a clear improvement in both segmentation and classification performance, also achieving competitive results when tested in public datasets. These results confirm that both the method and the data are important to obtain more accurate polyp representations.
Address Vancouver; October 2023
Corporate Author Thesis
Publisher 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 MICCAIW
Notes ISE Approved no
Call Number Admin @ si @ TGF2023 Serial 3837
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