scholarly journals State-of-the-Art Deep Learning in Cardiovascular Image Analysis

2019 ◽  
Vol 12 (8) ◽  
pp. 1549-1565 ◽  
Author(s):  
Geert Litjens ◽  
Francesco Ciompi ◽  
Jelmer M. Wolterink ◽  
Bob D. de Vos ◽  
Tim Leiner ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
State Of The Art ◽  
Cardiovascular Image

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals Quantifying Urban Surroundings Using Deep Learning Techniques: A New Proposal

Urban Science ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 78 ◽  
Author(s):  
Deepank Verma ◽  
Arnab Jana ◽  
Krithi Ramamritham
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
State Of The Art ◽  
Human Perception ◽  
Urban Spaces ◽  
Analysis Techniques ◽  
Learning Techniques ◽  
Environment Variables ◽  
Physical Features ◽  
Image Analysis Techniques

The assessments on human perception of urban spaces are essential for the management and upkeep of surroundings. A large part of the previous studies is dedicated towards the visual appreciation and judgement of various physical features present in the surroundings. Visual qualities of the environment stimulate feelings of safety, pleasure, and belongingness. Scaling such assessments to cover city boundaries necessitates the assistance of state-of-the-art computer vision techniques. We developed a mobile-based application to collect visual datasets in the form of street-level imagery with the help of volunteers. We further utilised the potential of deep learning-based image analysis techniques in gaining insights into such datasets. In addition, we explained our findings with the help of environment variables which are related to individual satisfaction and wellbeing.

Tutorial: Deep Learning Advancing the State-of-the-Art in Medical Image Analysis

2017 ◽  
pp. 6-7 ◽  
Author(s):  
Vincent Christlein ◽  
Florin C. Ghesu ◽  
Tobias Würfl ◽  
Andreas Maier ◽  
Fabian Isensee ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
The State

scholarly journals Advanced Computational Methods for Oncological Image Analysis

2021 ◽  
Vol 7 (11) ◽  
pp. 237
Author(s):  
Leonardo Rundo ◽  
Carmelo Militello ◽  
Vincenzo Conti ◽  
Fulvio Zaccagna ◽  
Changhee Han
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Computational Methods ◽  
State Of The Art ◽  
Original Research ◽  
Special Issue ◽  
Research Papers ◽  
Novel Algorithms

The Special Issue “Advanced Computational Methods for Oncological Image Analysis”, published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...]

scholarly journals Digital Naturalist Using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 2061-2065
Author(s):  
Aparna .
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Object Detection ◽  
Wild Species ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Semantic Segmentation ◽  
Model Image ◽  
Image Sensing ◽  
Learning Techniques

A naturalist is someone who studies the patterns of nature identify different kingdom of flora and fauna in the nature. Being able to identify the flora and fauna around us often leads to an interest in protecting wild species, collecting and sharing information about the species we see on our travels is very useful for conserving groups like NCC. Deep-learning based techniques and methods are becoming popular in digital naturalist studies, as their performance is superior in image analysis fields, such as object detection, image classification, and semantic segmentation. Deep-learning techniques have achieved state of-the -art performance for automatic segmentation of digital naturalist through multi-model image sensing. Our task as naturalist has grown widely in the field of natural-historians. It has increased from identification to saviours as well. Not only identifying flora and fauna but also to know about their habits, habitats, living and grouping lead to fetching services for protection as well.

scholarly journals Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

2021 ◽  
Author(s):  
Francesco Padovani ◽  
Benedikt Mairhoermann ◽  
Pascal Falter-Braun ◽  
Jette Lengefeld ◽  
Kurt M Schmoller
Keyword(s):  
Cell Cycle ◽  
Image Analysis ◽  
Deep Learning ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
State Of The Art ◽  
Single Cells ◽  
Live Cell ◽  
Cell Segmentation ◽  
User Friendly

Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. The open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation or downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC

scholarly journals State of the Art in Defect Detection Based on Machine Vision

2021 ◽  
Author(s):  
Zhonghe Ren ◽  
Fengzhou Fang ◽  
Ning Yan ◽  
You Wu
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Deep Learning ◽  
Machine Vision ◽  
Defect Detection ◽  
Visual Inspection ◽  
State Of The Art ◽  
Image Acquisition ◽  
Detection Algorithms ◽  
Further Development

AbstractMachine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Sign in / Sign up

Export Citation Format

Share Document