scholarly journals Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sixian You ◽  
Yi Sun ◽  
Lin Yang ◽  
Jaena Park ◽  
Haohua Tu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Receiver Operating Curve ◽  
Label Free ◽  
New Era ◽  
Virtual Histology ◽  
Learning Framework ◽  
Normal Human Breast ◽  
Normal Human ◽  
The Rich

AbstractRecent advances in label-free virtual histology promise a new era for real-time molecular diagnosis in the operating room and during biopsy procedures. To take full advantage of the rich, multidimensional information provided by these technologies, reproducible and reliable computational tools that could facilitate the diagnosis are in great demand. In this study, we developed a deep-learning-based framework to recognize cancer versus normal human breast tissue from real-time label-free virtual histology images, with a tile-level AUC (area under receiver operating curve) of 95% and slide-level AUC of 100% on unseen samples. Furthermore, models trained on a high-quality laboratory-generated dataset can generalize to independent datasets acquired from a portable intraoperative version of the imaging technology with a physics-based adapted design. Classification activation maps and final feature visualization revealed discriminative patterns, such as tumor cells and tumor-associated vesicles, that are highly associated with cancer status. These results demonstrate that through the combination of real-time virtual histopathology and a deep-learning framework, accurate real-time diagnosis could be achieved in point-of-procedure clinical applications.

scholarly journals Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 649
Author(s):  
Yifeng Liu ◽  
Wei Zhang ◽  
Wenhao Du
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Quality Data ◽  
Time Data ◽  
High Quality ◽  
Real Time System ◽  
High Quality Data ◽  
Learning Framework ◽  
Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

scholarly journals Real‐time comprehensive glass container inspection system based on deep learning framework

2019 ◽  
Vol 55 (3) ◽  
pp. 131-132 ◽  
Author(s):  
Qiaokang Liang ◽  
Shao Xiang ◽  
Jianyong Long ◽  
Wei Sun ◽  
Yaonan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Glass Container ◽  
Inspection System ◽  
Learning Framework ◽  
Container Inspection

scholarly journals Skin Diseases Prediction using Deep Learning Framework

2020 ◽  
Vol 8 (6) ◽  
pp. 4781-4784
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Dermatological Diseases ◽  
Learning Framework ◽  
Computer Aided ◽  
High Level ◽  
Almost All

Dermatological diseases are found to induce a serious impact on the health of millions of people as everyone is affected by almost all types of skin disorders every year. Since the human analysis of such diseases takes some time and effort, and current methods are only used to analyse singular types of skin diseases, there is a need for a more high-level computer-aided expertise in the analysis and diagnosis of multi-type skin diseases. This paper proposes an approach to use computer-aided techniques in deep learning neural networks such as Convolutional neural networks (CNN) and Residual Neural Networks (ResNet) to predict skin diseases real-time and thus provides more accuracy than other neural networks.

Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

2019 ◽  
Vol 31 (4) ◽  
pp. 799-814 ◽  
Author(s):  
Yanxi Zhang ◽  
Deyong You ◽  
Xiangdong Gao ◽  
Congyi Wang ◽  
Yangjin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Real Time ◽  
High Power ◽  
Real Time Monitoring ◽  
Disk Laser ◽  
Learning Framework

scholarly journals Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment

2020 ◽  
Vol 34 (04) ◽  
pp. 5029-5036
Author(s):  
You-Wei Luo ◽  
Chuan-Xian Ren ◽  
Pengfei Ge ◽  
Ke-Kun Huang ◽  
Yu-Feng Yu
Keyword(s):  
Deep Learning ◽  
Manifold Learning ◽  
Domain Adaptation ◽  
Global Approximation ◽  
Target Domain ◽  
Learning Framework ◽  
Unsupervised Domain Adaptation ◽  
Manifold Embedding ◽  
Rich Information ◽  
The Rich

Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, there are two issues to be further explored. First, the hard-assigned pseudo labels on the target domain are risky to the intrinsic data structure. Second, the batch-wise training manner in deep learning limits the description of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability consistently. As to the first problem, this method establishes a probabilistic discriminant criterion on the target domain via soft labels. Further, this criterion is extended to a global approximation scheme for the second issue; such approximation is also memory-saving. The manifold metric alignment is exploited to be compatible with the embedding space. A theoretical error bound is derived to facilitate the alignment. Extensive experiments have been conducted to investigate the proposal and results of the comparison study manifest the superiority of consistent manifold learning framework.

scholarly journals SIA: Selection Inference Using the Ancestral Recombination Graph

2021 ◽  
Author(s):  
Hussein A Hejase ◽  
Ziyi Mo ◽  
Leonardo Campagna ◽  
Adam Siepel
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Full Range ◽  
Genomic Diversity ◽  
Demographic Model ◽  
Ancestral Recombination Graph ◽  
Learning Framework ◽  
Novel Method ◽  
Rich Information ◽  
The Rich

Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.

scholarly journals Biopsy-free in vivo virtual histology of skin using deep learning

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jingxi Li ◽  
Jason Garfinkel ◽  
Xiaoran Zhang ◽  
Di Wu ◽  
Yijie Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Ex Vivo ◽  
Normal Skin ◽  
Ground Truth ◽  
Skin Neoplasms ◽  
Cellular Level ◽  
Label Free ◽  
Virtual Histology

AbstractAn invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.

Sign in / Sign up

Export Citation Format

Share Document