A Deep Learning Approach for Classifying Vulnerability Descriptions Using Self Attention Based Neural Network

Abstract BACKGROUND AND OBJECTIVE: Brain metastases have been found to account for one-fourth of all cancer metastases seen in clinics. Magnetic resonance imaging (MRI) is widely used for detecting brain metastases. Accurate detection of the brain metastases is critical to design radiotherapy to treat the cancer and monitor their progression or response to the therapy and prognosis. However, finding metastases on brain MRI is very challenging as many metastases are small and manifest as objects of weak contrast on the images. In this work we present a deep learning approach integrated with a classification scheme to detect cancer metastases to the brain on MRI. MATERIALS AND METHODS: We retrospectively extracted 101 metastases patients, equal to 1535 metastases on 10192 slices of images in a total of 336 scans from our PACS and manually marked the lesions on T1-weighted contrast enhanced MRI as the ground-truth. We then randomly separated the cases into training, validation, and test sets for developing and optimizing the deep learning neural network. We designed a 2-step computer-aided detection (CAD) pipeline by first applying a fast region-based convolutional neural network method (R-CNN) to sequentially process each slice of an axial brain MRI to find abnormal hyper-intensity that may correspond to a brain metastasis and, second, applying a random under sampling boost (RUSBoost) classification method to reduce the false positive metastases. RESULTS: The computational pipeline was tested on real brain images. A sensitivity of 97.28% and false positive rate of 36.25 per scan over the images were achieved by using the proposed method. CONCLUSION: Our results demonstrated the deep learning-based method can detect metastases in very challenging cases and can serve as CAD tool to help radiologists interpret brain MRIs in a time-constrained environment.

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Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach

Procedia Computer Science ◽

10.1016/j.procs.2018.05.069 ◽

2018 ◽

Vol 132 ◽

pp. 679-688 ◽

Cited By ~ 39

Author(s):

Sakshi Indolia ◽

Anil Kumar Goswami ◽

S.P. Mishra ◽

Pooja Asopa

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Conceptual Understanding ◽

Learning Approach

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Deep learning approach for automated cancer detection and tumor proportion score estimation of PD-L1 expression in lung adenocarcinoma

10.1101/2020.05.31.126797 ◽

2020 ◽

Author(s):

J. Wu ◽

C. Liu ◽

X. Liu ◽

W. Sun ◽

L. Li ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Lung Adenocarcinoma ◽

Cancer Detection ◽

Computational Method ◽

Learning Approach ◽

Automated Scoring ◽

Robust Performance ◽

Region Detection ◽

Whole Slide Images

AbstractBackgroundThis study proposed a computational method to detect the cancer areas and calculate the tumor proportion score (TPS) of PD-L1 immunohistochemistry (IHC) expression for lung adenocarcinoma based on deep learning and transfer learning.Patients and methodsPD-L1 22C3 and SP142 IHC slides of lung adenocarcinoma samples on digitized whole-slide images (WSI) database were employed. We build a deep convolutional neural network (DCNN) to automatically segment cancer regions. TPS was calculated based on segmented areas and then compared with the interpretations of pathologists.ResultsWe trained a DCNN model based on 22C3 dataset and fine-tuned it with SP142 dataset. We obtain a robust performance on cancer region detection on both datasets, with a sensitivity of 93.36% (22C3) and 92.80% (SP142) and a specificity of 93.97% (22C3) and 89.25% (SP142). With all the coefficient of determinations larger than 0.9 and Fleiss’ and Cohen’s Kappa larger than 0.8 (between mean or median of pathologists and TPS calculated by our method), we also found out the strong correlation between the TPS estimated by our computational method and estimation from multiple pathologists’ interpretations of 22C3 and SP142 respectively.ConclusionWe provide an AI method to efficiently predict cancer region and calculate TPS in PD-L1 IHC slide of lung adenocarcinoma on two different antibodies. It demonstrates the potential of using deep learning methods to conveniently access PD-L1 IHC status. In the future, we will further validate the AI tool for automated scoring PD-L1 in large volume samples.

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A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Ingenius ◽

10.17163/ings.n27.2022.04 ◽

2021 ◽

Author(s):

Lucas C. Lampier ◽

Yves L. Coelho ◽

Eliete M. O. Caldeira ◽

Teodiano Bastos-Filho

Keyword(s):

Neural Network ◽

Heart Rate ◽

Deep Learning ◽

Open Access ◽

Respiratory Rate ◽

Analytical Methods ◽

Deep Neural Network ◽

Learning Approach ◽

Genetic Optimization ◽

Median Error

This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.

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