Discrimination Between Invasive and In situ Melanomas Using Clinical Close-up Images and a de novo Convolutional Neural Network

Author(s):  
Sam Polesie
2021 ◽  
Vol 141 (10) ◽  
pp. S196
Author(s):  
S. Polesie ◽  
M. Gillstedt ◽  
G. Ahlgren ◽  
H. Ceder ◽  
J Dahlén Gyllencreutz ◽  
...  

Author(s):  
Maolin Wang ◽  
Kelvin C. M. Lee ◽  
Bob M. F. Chung ◽  
Sharatchandra Varma Bogaraju ◽  
Ho-Cheung Ng ◽  
...  

2021 ◽  
Vol 68 ◽  
pp. 347-355
Author(s):  
Qihang Fang ◽  
Zhenbiao Tan ◽  
Hui Li ◽  
Shengnan Shen ◽  
Sheng Liu ◽  
...  

Author(s):  
Aniruddha Gaikwad ◽  
Farhad Imani ◽  
Prahalad Rao ◽  
Hui Yang ◽  
Edward Reutzel

Abstract The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: 1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall (l) to its thickness (t) defined as the aspect ratio (length-to-thickness ratio, l/t), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine. 2) Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.


2020 ◽  
Vol 20 (15) ◽  
pp. 8287-8296 ◽  
Author(s):  
Siliang Lu ◽  
Gang Qian ◽  
Qingbo He ◽  
Fang Liu ◽  
Yongbin Liu ◽  
...  

2020 ◽  
Vol 26 (S2) ◽  
pp. 1720-1721
Author(s):  
Joshua Vincent ◽  
Sreyas Mohan ◽  
Carlos Fernandez-Granda ◽  
Peter Crozier

2020 ◽  
Author(s):  
Haidong Yan ◽  
Aureliano Bombarely ◽  
Song Li

AbstractMotivationTransposable elements (TEs) classification is an essential step to decode their roles in genome evolution. With a large number of genomes from non-model species becoming available, accurate and efficient TE classification has emerged as a new challenge in genomic sequence analysis.ResultsWe developed a novel tool, DeepTE, which classifies unknown TEs using convolutional neural networks. DeepTE transferred sequences into input vectors based on k-mer counts. A tree structured classification process was used where eight models were trained to classify TEs into super families and orders. DeepTE also detected domains inside TEs to correct false classification. An additional model was trained to distinguish between non-TEs and TEs in plants. Given unclassified TEs of different species, DeepTE can classify TEs into seven orders, which include 15, 24, and 16 super families in plants, metazoans, and fungi, respectively. In several benchmarking tests, DeepTE outperformed other existing tools for TE classification. In conclusion, DeepTE successfully leverages convolutional neural network for TE classification, and can be used to precisely identify and annotate TEs in newly sequenced eukaryotic genomes.AvailabilityDeepTE is accessible at https://github.com/LiLabAtVT/[email protected]


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