检索结果-内蒙古大学图书馆

Single probe light reflectance spectroscopy and parameter spectrum feature extraction in experimental skin cancer detection and classification

引用

JOURNAL OF BIOPHOTONICS 2023年第8期16卷 e202300001-e202300001页

作者： Abookasis, David Shemesh, David Litwin, Arik Siegelmann, Hava T. Didkovsky, Elena Ad-El, Dean D. Ariel Univ Dept Elect & Elect Engn Ariel Israel Rabin Med Ctr Dept Plast Surg & Burns Petah Tiqwa Israel Tel Aviv Univ Sackler Fac Med Tel Aviv Israel Univ Massachusetts Amherst Dept Comp Sci Amherst MA USA Univ Massachusetts Amherst Program Neurosci & Behav Amherst MA USA MBZ Univ AI Dept Machine Learning Abu Dhabi U Arab Emirates Ariel Univ Dept Elect & Elect Engn IL-407000 Ariel Israel Rabin Med Ctr Dept Plast Surg & Burns Beilinson Campus IL-4941492 Petah Tiqwa Israel

Skin cancer, an anomalous development of skin cells in the epidermis, is among the most common types of cancer worldwide. Because of its clinical importance and to improve early diagnosis and patient management, there is an urgent need to develop noninvasive, accurate medical diagnostic tools. To this aim, light reflectance spectroscopy over the visible and near-infrared spectral range (400-1000 nm) based on a single-fiber six-around-one optical probe was applied to extract nine features used for diagnostics. These features include skewness, entropy, energy, kurtosis, scattering amplitude, and others, and are spread over each of four different spectral signatures, namely, light reflectance, absorbance, scattering profile approximation, and absorption/scattering ratio. Our preliminary studies focused on 11 adult patients with diagnoses of malignant melanoma (n = 4), basal cell carcinoma (n = 5), and squamous cell carcinoma (n = 2) in a variety of locations on the body. Measurements were taken first in vivo before surgery, at the site of the lesion and from healthy skin of the same patient, and ex vivo after surgical excision, where the lesion was rinsed in saline solution and measurements of the reflected light from the "inside" facing plane of the tissue were taken in the same manner. Overall, experimental results demonstrate that by examining a variety of wavebands, features, and statistical metrics, we can detect and distinguish cancer from normal tissue and different cancer subtypes. Nevertheless, discrepancies in results between in vivo and ex vivo tissue were observed and explanations for these discrepancies are discussed.

关键词： cancer detection and classification extracted features reflectance spectroscopy single optical probe

来源：评论

学校读者我要写书评

暂无评论

Tumor response prediction in ⁹⁰Y radioembolization with PET-based radiomics features and absorbed dose metrics

引用

EJNMMI PHYSICS 2020年第1期7卷 74页

作者： Wei, Lise Cui, Can Xu, Jiarui Kaza, Ravi El Naqa, Issam Dewaraja, Yuni K. Univ Michigan Appl Phys Program Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn Ann Arbor MI 48109 USA Univ Michigan Dept Radiol Ann Arbor MI 48109 USA Univ Michigan Dept Radiat Oncol Ann Arbor MI 48109 USA H Lee Moffitt Canc Ctr & Res Inst Machine Learning Dept Tampa FL USA

Purpose To evaluate whether lesion radiomics features and absorbed dose metrics extracted from post-therapy Y-90 PET can be integrated to better predict outcomes in microsphere radioembolization of liver malignancies Methods Given the noisy nature of Y-90 PET, first, a liver phantom study with repeated acquisitions and varying reconstruction parameters was used to identify a subset of robust radiomics features for the patient analysis. In 36 radioembolization procedures, Y-90 PET/CT was performed within a couple of hours to extract 46 radiomics features and estimate absorbed dose in 105 primary and metastatic liver lesions. Robust radiomics modeling was based on bootstrapped multivariate logistic regression with shrinkage regularization (LASSO) and Cox regression with LASSO. Nested cross-validation and bootstrap resampling were used for optimal parameter/feature selection and for guarding against overfitting risks. Spearman rank correlation was used to analyze feature associations. Area under the receiver-operating characteristics curve (AUC) was used for lesion response (at first follow-up) analysis while Kaplan-Meier plots and c-index were used to assess progression model performance. Models with absorbed dose only, radiomics only, and combined models were developed to predict lesion outcome. Results The phantom study identified 15/46 reproducible and robust radiomics features that were subsequently used in the patient models. A lesion response model with zone percentage (ZP) and mean absorbed dose achieved an AUC of 0.729 (95% CI 0.702-0.758), and a progression model with zone size nonuniformity (ZSN) and absorbed dose achieved a c-index of 0.803 (95% CI 0.790-0.815) on nested cross-validation (CV). Although the combined models outperformed the radiomics only and absorbed dose only models, statistical significance was not achieved with the current limited data set to establish expected superiority. Conclusion We have developed new lesion-level response and progre

关键词： Y-90 PET imaging Liver cancer Radiomics Absorbed dose Radioembolization

来源：评论

学校读者我要写书评

暂无评论

Chemical and biological language models in molecular design: opportunities, risks and scientific reasoning

引用

FUTURE SCIENCE OA 2024年第1期10卷 FSO957页

作者： Bajorath, Juergen Rhein Friedrich Wilhelms Univ Lamarr Inst Machine Learning & Artificial Intellig Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med ChemB IT Friedrich Hirzebruch Allee 5-6 Bonn D-53115 Germany

来源：评论

学校读者我要写书评

暂无评论

Hybrid Auxiliary Field Quantum Monte Carlo for Molecular Systems

arXiv

引用

arXiv 2022年

作者： Chen, Yixiao Zhang, Linfeng Weinan, E. Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100084 China Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States

We propose a quantum Monte Carlo approach to solve the many-body Schrödinger equation for the electronic ground state. The method combines optimization from variational Monte Carlo and propagation from auxiliary field quantum Monte Carlo, in a way that significantly alleviates the sign problem. In application to molecular systems, we obtain highly accurate results for configurations dominated by either dynamic or static electronic correlation. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

Deep learning identifies synergistic drug combinations for treating COVID-19

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2021年第39期118卷 1页

作者： Jin, Wengong Stokes, Jonathan M. Eastman, Richard T. Itkin, Zina Zakharov, Alexey, V Collins, James J. Jaakkola, Tommi S. Barzilay, Regina MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Inst Med Engn & Sci Synthet Biol Ctr Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA Natl Ctr Adv Translat Sci Div Preclin Innovat Rockville MD 20850 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.

关键词： deep learning drug discovery drug synergy SARS-CoV-2

来源：评论

学校读者我要写书评

暂无评论

Eradicating Bacterial Persisters with Combinations of Strongly and Weakly Metabolism-Dependent Antibiotics

引用

CELL CHEMICAL BIOLOGY 2020年第12期27卷 1544-+页

作者： Zheng, Erica J. Stokes, Jonathan M. Collins, James J. Harvard Univ Program Chem Biol Cambridge MA 02138 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA MIT Dept Biol Engn Inst Med Engn & Sci Cambridge MA 02139 USA MIT Synthet Biol Ctr Cambridge MA 02139 USA MIT Machine Learning Pharmaceut Discovery & Synth Con Cambridge MA 02139 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

The vast majority of bactericidal antibiotics display poor efficacy against bacterial persisters, cells that are in ametabolically repressed state. Molecules that retain their bactericidal functions against such bacteria often display toxicity to human cells, which limits treatment options for infections caused by persisters. Here, we leverage insight into metabolism-dependent bactericidal antibiotic efficacy to design antibiotic combinations that sterilize both metabolically active and persister cells, while minimizing the antibiotic concentrations required. These rationally designed antibiotic combinations have the potential to improve treatments for chronic and recurrent infections.

关键词： antibiotics bacterial persisters metabolism antibiotic combinations

来源：评论

学校读者我要写书评

暂无评论

Ant Colony Optimization based Medical Image Preservation and Segmentation

Ant Colony Optimization based Medical Image Preservation and...

引用

Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging Optical design Image edge detection Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Software Support Tools for Reirradiation: A Report From Reirradiation Collaborative Group (ReCOG) 2024

引用

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS 2025年第1期122卷 181-185页

作者： Matrosic, Charles K. Andrzejewski, Piotr Bergman, Alanah Chng, Nick El Naqa, Issam Freislederer, Philipp Hoffmann, Lone Hope, Andrew Jacqmin, Dustin Kelly, Colin Kuo, Licheng Lin, Haibo Mayo, Charles Murrell, Donna H. Oden, Jakob Svensson, Stina Thompson, Stephen Osorio, Eliana Vasquez West, Nick Wicha, Roman Yorke, Ellen Appelt, Ane L. Paradis, Kelly C. Univ Michigan Dept Radiat Oncol Ann Arbor MI 48109 USA MedAustron Ion Therapy Ctr Wiener Neustadt Austria Univ British Columbia Dept Radiat Oncol BC Canc Vancouver Vancouver BC Canada Univ British Columbia Dept Med Phys BC Canc Prince George Prince George BC Canada H Lee Moffitt Canc Ctr & Res Inst Machine Learning Dept Tampa FL USA Brainlab AG Munich Germany Aarhus Univ Hosp Dept Oncol Aarhus Denmark Univ Toronto Univ Hlth Network Dept Radiat Oncol Radiat Med ProgramPrincess Margaret Canc Ctr Toronto ON Canada Univ Wisconsin Dept Human Oncol Madison WI USA St Lukes Radiat Oncol Network Dublin Ireland Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY USA New York Proton Ctr New York NY USA Western Univ Dept Oncol London ON Canada London Hlth Sci Ctr London ON Canada RaySearch Labs AB Stockholm Sweden Varian Med Syst Palo Alto CA USA Univ Manchester Div Canc Sci Manchester England Newcastle Tyne Hosp Trust Northern Ctr Canc Care Newcastle Upon Tyne England Univ Leeds Leeds Inst Med Res St Jamess Leeds England

来源：评论

学校读者我要写书评

暂无评论

A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution

引用

NATURE NEUROSCIENCE 2022年第12期25卷 1724-+页

作者： Zhu, Feng Grier, Harrison A. Tandon, Raghav Cai, Changjia Agarwal, Anjali Giovannucci, Andrea Kaufman, Matthew T. Pandarinath, Chethan Emory Univ Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Grad Div Biol & Biomed Sci Neurosci Grad Program Atlanta GA 30322 USA Univ Chicago Comm Computat Neurosci Chicago IL 60637 USA Univ North Carolina Chapel Hill & North Carolina Joint Biomed Engn Dept Chapel Hill NC 27514 USA Univ North Carolina Neurosci Ctr Chapel Hill NC 27515 USA North Carolina State Univ Closed Loop Engn Adv Rehabil CLEAR Raleigh NC 27695 USA Univ Chicago Dept Organismal Biol & Anat 1025 E 57Th St Chicago IL 60637 USA Univ Chicago Neurosci Inst Chicago IL 60637 USA Emory Univ Dept Neurosurg Atlanta GA 30322 USA Georgia Inst Technol Ctr Machine Learning Atlanta GA 30332 USA

In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced. Zhu et al. develop a deep learning method to precisely infer single-trial neural dynamics from calcium imaging with subframe temporal resolution, which shows improvement over the state-of-the-art methods in capturing high-frequency dynamics and predicting behavior.

关键词： Dynamical systems Fluorescence imaging machine learning Neural decoding

来源：评论

学校读者我要写书评

暂无评论

Fostering natural language question answering over knowledge bases in oncology EHR 32

Fostering natural language question answering over knowledge...

引用

32nd IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS)

作者： Schwertner, M. A. Rigo, S. J. Araujo, D. A. Silva, A. B. Eskofier, B. Univ Vale Rio Sinos UNISINOS Appl Comp Grad Program Sao Leopoldo Brazil FAU Erlangen Nurnberg Dept Comp Sci Machine Learning & Data Analyt Lab Erlangen Germany

ISBN: (纸本)9781728122861

This paper presents an approach for natural language question answering over a knowledge base generated by a medical texts information extraction process. The primary objective is to present a solution to help practitioners in oncology healthcare clinical environment with an intuitive method to access stored data. We identify health professional's needs in terms of information and interface with EHR systems. After that, we demonstrate a proposal to allow the integration of information extraction from clinical notes, knowledge base generation, and natural language question answering. The primary contributions are the identification of a solution to health professionals needs regarding usability in information access, and the demonstration of advantages obtained in representing health contents in a knowledge base.

关键词： Information Extraction Question Answering Clinical systems Oncology EHR

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：