| Records |
| Author |
Smriti Joshi; Richard Osuala; Carlos Martin-Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera |
| Title |
nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging |
Type |
Conference Article |
| Year |
2022 |
Publication |
International MICCAI Brainlesion Workshop |
Abbreviated Journal |
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| Volume |
12963 |
Issue |
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Pages |
540–551 |
| Keywords |
Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN |
| Abstract |
In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1. |
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LNCS |
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MICCAIW |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ JOM2022 |
Serial |
3800 |
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| Author |
Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds) |
| Title |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 |
Type |
Book Whole |
| Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. |
Abbreviated Journal |
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| Volume |
13639 |
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ICFHR 2022, Hyderabad, India, December 4–7, 2022 |
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Springer |
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Editor |
Utkarsh Porwal; Alicia Fornes; Faisal Shafait |
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LNCS |
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ISBN |
978-3-031-21648-0 |
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Conference |
ICFHR |
| Notes |
DAG |
Approved |
no |
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Admin @ si @ PFS2022 |
Serial |
3809 |
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| Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
| Title |
Cross-Spectral Image Processing |
Type |
Book Chapter |
| Year |
2022 |
Publication |
Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
23-34 |
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| Abstract |
Although this book is on IR computer vision and its main focus lies on IR image and video processing and analysis, a special attention is dedicated to cross-spectral image processing due to the increasing number of publications and applications in this domain. In these cross-spectral frameworks, IR information is used together with information from other spectral bands to tackle some specific problems by developing more robust solutions. Tasks considered for cross-spectral processing are for instance dehazing, segmentation, vegetation index estimation, or face recognition. This increasing number of applications is motivated by cross- and multi-spectral camera setups available already on the market like for example smartphones, remote sensing multispectral cameras, or multi-spectral cameras for automotive systems or drones. In this chapter, different cross-spectral image processing techniques will be reviewed together with possible applications. Initially, image registration approaches for the cross-spectral case are reviewed: the registration stage is the first image processing task, which is needed to align images acquired by different sensors within the same reference coordinate system. Then, recent cross-spectral image colorization approaches, which are intended to colorize infrared images for different applications are presented. Finally, the cross-spectral image enhancement problem is tackled by including guided super resolution techniques, image dehazing approaches, cross-spectral filtering and edge detection. Figure 3.1 illustrates cross-spectral image processing stages as well as their possible connections. Table 3.1 presents some of the available public cross-spectral datasets generally used as reference data to evaluate cross-spectral image registration, colorization, enhancement, or exploitation results. |
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Springer |
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SLCV |
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978-3-031-00698-2 |
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| Notes |
MSIAU; MACO |
Approved |
no |
| Call Number |
Admin @ si @ TSH2022b |
Serial |
3805 |
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| Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
| Title |
Detection, Classification, and Tracking |
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Book Chapter |
| Year |
2022 |
Publication |
Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
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Issue |
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Pages |
35-58 |
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| Abstract |
Automatic image and video exploitation or content analysis is a technique to extract higher-level information from a scene such as objects, behavior, (inter-)actions, environment, or even weather conditions. The relevant information is assumed to be contained in the two-dimensional signal provided in an image (width and height in pixels) or the three-dimensional signal provided in a video (width, height, and time). But also intermediate-level information such as object classes [196], locations [197], or motion [198] can help applications to fulfill certain tasks such as intelligent compression [199], video summarization [200], or video retrieval [201]. Usually, videos with their temporal dimension are a richer source of data compared to single images [202] and thus certain video content can be extracted from videos only such as object motion or object behavior. Often, machine learning or nowadays deep learning techniques are utilized to model prior knowledge about object or scene appearance using labeled training samples [203, 204]. After a learning phase, these models are then applied in real world applications, which is called inference. |
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Springer |
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SLCV |
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978-3-031-00698-2 |
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| Notes |
MSIAU; MACO |
Approved |
no |
| Call Number |
Admin @ si @ TSH2022c |
Serial |
3806 |
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| Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
| Title |
Image and Video Enhancement |
Type |
Book Chapter |
| Year |
2022 |
Publication |
Computer Vision in the Infrared Spectrum. Synthesis Lectures on Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
9-21 |
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Image and video enhancement aims at improving the signal quality relative to imaging artifacts such as noise and blur or atmospheric perturbations such as turbulence and haze. It is usually performed in order to assist humans in analyzing image and video content or simply to present humans visually appealing images and videos. However, image and video enhancement can also be used as a preprocessing technique to ease the task and thus improve the performance of subsequent automatic image content analysis algorithms: preceding dehazing can improve object detection as shown by [23] or explicit turbulence modeling can improve moving object detection as discussed by [24]. But it remains an open question whether image and video enhancement should rather be performed explicitly as a preprocessing step or implicitly for example by feeding affected images directly to a neural network for image content analysis like object detection [25]. Especially for real-time video processing at low latency it can be better to handle image perturbation implicitly in order to minimize the processing time of an algorithm. This can be achieved by making algorithms for image content analysis robust or even invariant to perturbations such as noise or blur. Additionally, mistakes of an individual preprocessing module can obviously affect the quality of the entire processing pipeline. |
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Springer |
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SLCV |
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MSIAU; MACO |
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no |
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Admin @ si @ TSH2022a |
Serial |
3807 |
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