检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Paul, Richard Seiffarth, Johannes Scharr, Hanno Nöh, Katharina IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany RWTH Aachen University Aachen Germany

Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results. © 2024, CC BY.

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

DEEP learning BASED PREDICTION OF SUN-INDUCED FLUORESCENCE FROM HYPLANT IMAGERY

DEEP LEARNING BASED PREDICTION OF SUN-INDUCED FLUORESCENCE F...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Buffat, Jim Pato, Miguel Alonso, Kevin Auer, Stefan Carmona, Emiliano Maier, Stefan Mueller, Rupert Rademske, Patrick Rascher, Uwe Scharr, Hanno Forschungszentrum Julich GmbH Inst Bio & Geosci IBG 2 Plant Sci Julich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst Earth Observat Ctr Oberpfaffenhofen Germany European Space Agcy ESA RHEA Grp I-00044 Frascati Italy Forschungszentrum Julich GmbH Inst Adv Simulat IAS 8 Data Analyt & Machine Learning Julich Germany

ISBN: (纸本)9798350320107

The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.

关键词： solar-induced fluorescence hyperspectral sensors radiative transfer machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning algorithms to classify Fitzpatrick skin types for smartphone based NIRS imaging device 15

Deep learning algorithms to classify Fitzpatrick skin types ...

引用

Next-Generation Spectroscopic Technologies XV 2023

作者： Leizaola, Daniela Sobhan, Masrur Kaile, Kacie Mondal, Ananda Mohan Godavarty, Anuradha Optical Imaging Laboratory Department of Biomedical Engineering Florida International University 10555 W Flagler St MiamiFL33174 United States Machine Learning and Data Analytics Group Knight Foundation School Computing and Information Sciences Florida International University 11200 SW 8th St MiamiFL33199 United States

ISBN: (数字)9781510661479

ISBN: (纸本)9781510661462

Non-contact imaging modalities for monitoring wound health could supplement the current standard which is a visual inspection by clinicians. Recently, a smartphone oxygenation tool (SPOT) has been developed for physiological imaging of tissue oxygenation changes in response to treatment. However, upon visual inspection of the wound bed and surrounding area, there are variations of pigmentation. Melanin concentration is a highly absorbing chromophore that can impact spatial oxygenation measurements. The objective of this study was to classify the six Fitzpatrick Skin Types (FST) by applying deep learning techniques prior to correcting SPOT’s oxygenation maps. In this IRB-approved study, control subjects were imaged on seven skin locations of varying FST (I-VI) under three different lighting conditions using SPOT device’s camera. A benchmark dataset with samples of 28 × 28 pixel images of human subjects’ feet was developed in three color spaces (R-G-B, Y-Cb-Cr, L-a-b). A deep learning algorithm, specifically a convolutional neural network (CNN), was used to classify skin into six FST (classes). Preliminary results showed the skin types on control subjects’ feet could be classified using deep learning with hyperparameter tuning with accuracies of > 82%. Our ongoing efforts are focused on extensive in-vivo studies on control subjects of FST I-VI on feet towards future implementation of the technology for diabetic wounds and oxygenation mapping using the SPOT device. © 2023 SPIE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

machine learning algorithms to classify Fitzpatrick skin types during tissue oxygenation mapping

Machine learning algorithms to classify Fitzpatrick skin typ...

引用

Clinical and Translational Biophotonics, Translational 2022

作者： Kaile, Kacie Sobhan, Masrur Mondal, Ananda Godavarty, Anuradha Department of Biomedical Engineering Optical Imaging Laboratory Florida International University 10555 West Flagler St MiamiFL33174 United States Machine Learning and Data Analytics Group Knight Foundation School of Computing and Information Sciences Florida International University 11200 SW 8th St MiamiFL33199 United States

A machine learning approach is implemented to label different skin tones towards melanin correction of tissue oxygenation maps, when using a smartphone based near-infrared (NIR) imaging device. © 2022 Optica Publ... 详细信息

ISBN: (纸本)9781557528209

关键词： Tissue

来源：评论

学校读者我要写书评

暂无评论

Robust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth

Robust Approximate Characterization of Single-Cell Heterogen...

引用

IEEE International Symposium on Biomedical Imaging

作者： Richard D. Paul Johannes Seiffarth Hanno Scharr Katharina Nöh IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany Computational Systems Biotechnology (AVT.CSB) RWTH Aachen University Aachen Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

关键词： Instance segmentation Image resolution Microscopy Sociology Stochastic processes Biomedical measurement Biology

来源：评论

学校读者我要写书评

暂无评论

FAST machine learning SIMULATOR OF AT-SENSOR RADIANCES FOR SOLAR-INDUCED FLUORESCENCE RETRIEVAL WITH DESIS AND HYPLANT

FAST MACHINE LEARNING SIMULATOR OF AT-SENSOR RADIANCES FOR S...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Pato, Miguel Alonso, Kevin Auer, Stefan Buffat, Jim Carmona, Emiliano Maier, Stefan Mueller, Rupert Rademske, Patrick Rascher, Uwe Scharr, Hanno German Aerosp Ctr DLR Earth Observat Ctr Remote Sensing Technol Inst Oberpfaffenhofen Germany European Space Agcy ESA RHEA Grp Largo Galileo Galilei I-00044 Frascati Italy Forschungszentrum Julich GmbH Inst Bio & Geosci IBG2 Plant Sci Julich Germany Forschungszentrum Julich GmbH Inst Adv Simulat IAS8 Data Analyt & Machine Learning Julich Germany

ISBN: (纸本)9798350320107

In many remote sensing applications the measured radiance needs to be corrected for atmospheric effects to study surface properties such as reflectance, temperature or emission features. The correction often applies radiative transfer to simulate atmospheric propagation, a time-consuming step usually done offline. In principle, an efficient machine learning (ML) model can accelerate the simulation step. This is the goal pursued here in the context of solar-induced fluorescence (SIF) emitted by vegetation around the O-2-A band using the spaceborne DESIS and airborne HyPlant spectrometers. We present an ML simulator of at-sensor radiances trained on synthetic spectra and describe its performance in detail. The simulator is fast and accurate, constituting a promising alternative to a full-fledged, lengthy radiative transfer code for SIF retrieval in the O-2-A band with DESIS and HyPlant.

关键词： solar-induced fluorescence hyperspectral sensors radiative transfer machine learning

来源：评论

学校读者我要写书评

暂无评论

Retrieval of sun-induced plant fluorescence in the O2-A absorption band from DESIS imagery

arXiv

引用

arXiv 2024年

作者： Buffat, Jim Pato, Miguel Alonso, Kevin Auer, Stefan Carmona, Emiliano Maier, Stefan Müller, Rupert Rademske, Patrick Rascher, Uwe Scharr, Hanno Forschungszentrum Jülich GmbH Institute of Bio- and Geosciences IBG-2: Plant Sciences Jülich Germany Oberpfaffenhofen Germany Largo Galileo Galilei Frascati00044 Italy Forschungszentrum Jülich GmbH Institute of Advanced Simulations IAS-8: Data Analytics and Machine Learning Jülich Germany

We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation (r2 = 0.6) to high-quality airborne estimates of sun-induced fluorescence (SIF). SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESA’s upcoming FLEX mission we develop a method for SIF retrieval in the O2-A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of 0.78 mW nm−1 sr−1 m−2 © 2024, CC BY.

关键词： Physiological models

来源：评论

学校读者我要写书评

暂无评论

Abdominal multi-organ segmentation using multi-scale and context-aware neural networks

引用

IFAC JOURNAL OF SYSTEMS AND CONTROL 2024年 27卷

作者： Song, Yuhan Elibol, Armagan Chong, Nak Young Japan Adv Inst Sci & Technol Sch Informat Sci Nomi 9231292 Japan Forschungszentrum Julich Inst Adv Simulat IAS 8 Data Analyt & Machine Learning D-52428 Julich Germany

Recent advancements in AI have significantly enhanced smart diagnostic methods, bringing us closer to achieving end -to -end diagnosis. Ultrasound image segmentation plays a crucial role in this diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we consider two inherent features of ultrasound images: (1) different organs and tissues vary in spatial sizes, and (2) the anatomical structures inside the human body form a relatively constant spatial relationship. Based on those two ideas, we proposed two segmentation models combining multi -scale convolution neural network backbones and a spatial context feature extractor. We discuss two backbone structures to extract anatomical structures of different scales: the Feature Pyramid Network (FPN) backbone and the Trident Network backbone. Moreover, we show how Spatial Recurrent Neural Network (SRNN) is implemented to extract the spatial context features in abdominal ultrasound images. Our proposed model has achieved dice coefficient score of 0.919 and 0.931, respectively. (c) 2024 Elsevier Ltd. All rights reserved.

关键词： Medical imaging and processing Diagnostic ultrasound Image segmentation Feature pyramid network Trident network

来源：评论

学校读者我要写书评

暂无评论

Spatio-spectral deconvolution for high resolution spectral imaging with an application to the estimation of sun-induced fluorescence

引用

REMOTE SENSING OF ENVIRONMENT 2021年 267卷 112718-112718页

作者： Scharr, Hanno Rademske, Patrick Alonso, Luis Cogliati, Sergio Rascher, Uwe Forschungszentrum Julich Inst Bio & Geosci IBG IBG 2 Plant Sci D-52425 Julich Germany Forschungszentrum Julich Inst Adv Simulat IAS IAS 8 Data Analyt & Machine Learning D-52425 Julich Germany Univ Valencia Image Proc Lab Valencia Spain Univ Milano Bicocca DISAT Remote Sensing Environm Dynam Lab Pzza Sci 1 I-20126 Milan Italy

We propose a signal deconvolution procedure for imaging spectrometer data, where a measured point spread function (PSF) is deconvolved itself before being used for deconvolution of the signal. We evaluate the effectiveness of our procedure for improvement of the spatio-spectral signal, as well as our target application, i.e. estimation of sun-induced fluorescence (SIF). Imaging spectrometers are well established instruments for remote sensing. When used for scientific purposes these instruments are usually calibrated on a regular basis. In our case the point spread function of the optics is measured in an elaborate procedure with a tunable monochromator point light source. PSFs are measured at different pixel positions of the imaging sensor, i.e. at different spatiospectral locations, and averaged in order to get an as accurate PSF as possible. We investigate error sources in this calibration process by simulating the procedure in silico. Averaging as well as the spectral and spatial width of the point source introduce some smoothness in the measured PSF. We propose corrective measures, i.e. deconvolution of the PSF itself and median instead of mean averaging, leading to a set of sharper PSFs. We test the performance of these PSFs in deconvolving simulated as well as real hyperspectral images. For deconvolution we test a set of well-known, off the shelf deconvolution algorithms. Quantitatively in terms of PSNR (Peak Signal to Noise Ratio) a combination of Wiener filtering and sharpened PSFs yields strongest improvements, while using Wiener filtering with non-sharpened PSFs even deteriorates the signal. Comparing deconvolution results of the simulated data with results of real data reveals, that visually very similar effects can be observed. This well supports the assumption, that our findings are also valid for real spatio-spectral data. Surprisingly, the choice of PSF, sharpened or not, is of little effect for SIF estimation with the iFLD algorithm in the O(2)A ban

关键词： Sun-induced fluorescence (SIF) Deconvolution of measured PSF and signal Calibration simulation Quantitative analysis O(2)A O2B Imaging spectrometer

来源：评论

学校读者我要写书评

暂无评论

Deep learning Based Prediction of Sun-Induced Fluorescence from Hyplant Imagery

Deep Learning Based Prediction of Sun-Induced Fluorescence f...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jim Buffat Miguel Pato Kevin Alonso Stefan Auer Emiliano Carmona Stefan Maier Rupert Müller Patrick Rademske Uwe Rascher Hanno Scharr Forschungszentrum Jülich GmbH Institute of Bio- and Geosciences IBG-2: Plant Sciences Jülich Germany German Aerospace Center (DLR) Earth Observation Center Remote Sensing Technology Institute Oberpfaffenhofen Germany RHEA Group c/o European Space Agency (ESA) Frascati Italy Forschungszentrum Jülich GmbH Institute of Advanced Simulations IAS-8: Data Analytics and Machine Learning Jülich Germany

The retrieval of sun-induced fluorescence (SIF) from hyper-spectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：