scholarly journals Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao Fu ◽  
Weiming Mi ◽  
Boju Pan ◽  
Yucheng Guo ◽  
Junjie Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Network Architecture ◽  
Extraction Methods ◽  
Ductal Adenocarcinoma ◽  
Deep Convolutional Neural Networks ◽  
Histopathological Images ◽  
Feature Based ◽  
Detection And Diagnosis ◽  
Histopathology Image Analysis

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.

scholarly journals Syndecans and Pancreatic Ductal Adenocarcinoma

Biomolecules ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 349
Author(s):  
Nausika Betriu ◽  
Juan Bertran-Mas ◽  
Anna Andreeva ◽  
Carlos E. Semino
Keyword(s):  
Extracellular Matrix ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cell Types ◽  
Ductal Adenocarcinoma ◽  
Physiological Processes ◽  
Extracellular Matrix Components ◽  
Detection And Diagnosis ◽  
And Migration ◽  
Early Detection And Diagnosis

Pancreatic Ductal Adenocarcinoma (PDAC) is a fatal disease with poor prognosis because patients rarely express symptoms in initial stages, which prevents early detection and diagnosis. Syndecans, a subfamily of proteoglycans, are involved in many physiological processes including cell proliferation, adhesion, and migration. Syndecans are physiologically found in many cell types and their interactions with other macromolecules enhance many pathways. In particular, extracellular matrix components, growth factors, and integrins collect the majority of syndecans associations acting as biochemical, physical, and mechanical transducers. Syndecans are transmembrane glycoproteins, but occasionally their extracellular domain can be released from the cell surface by the action of matrix metalloproteinases, converting them into soluble molecules that are capable of binding distant molecules such as extracellular matrix (ECM) components, growth factor receptors, and integrins from other cells. In this review, we explore the role of syndecans in tumorigenesis as well as their potential as therapeutic targets. Finally, this work reviews the contribution of syndecan-1 and syndecan-2 in PDAC progression and illustrates its potential to be targeted in future treatments for this devastating disease.

A New Feature Extraction Method for Bone-Conducted Life Sounds Based on F-Ratio

2013 ◽  
Vol 411-414 ◽  
pp. 1598-1604
Author(s):  
Ye Teng An ◽  
Hong Cui Wang ◽  
Song Gun Hyon ◽  
Sai Chen ◽  
Jian Wu Dang
Keyword(s):  
Feature Extraction ◽  
Recognition Task ◽  
Extraction Methods ◽  
Frequency Region ◽  
Frequency Filter ◽  
Feature Extraction Method ◽  
Discriminative Feature ◽  
Frequency Components ◽  
Feature Based ◽  
New Feature

Bone-conducted life sounds are useful for monitoring human healthy situation. Although a number of feature extraction methods were proposed for air-conducted speech, they may not meet the requirements of the recognition task for bone-conducted life sounds since there is a large difference between air-conducted speech and bone-conducted life sounds. In order to obtain features that can characterize bone-conducted signals, in this study, we first analyze the property of bone-conducted life sounds itself and compare each kind of life sounds in the frequency region. Then we adopt the methods of F-ratio and improved F-ratio separately to measure the dependences between frequency components and characteristics of life sounds. According to the result of analysis, we design a new adaptive frequency filter to extract the desired discriminative feature. The new feature is combined with the Hidden Markov Model and applied to classify different kinds of bone-conducted life sounds. The experimental results show that the error rate using the proposed feature based on State mean F-ratio is reduced by 7.2% compared with the MFCC feature.

scholarly journals Fundus Image Registration Technique Based on Local Feature of Retinal Vessels

2021 ◽  
Vol 11 (23) ◽  
pp. 11201
Author(s):  
Roziana Ramli ◽  
Khairunnisa Hasikin ◽  
Mohd Yamani Idna Idris ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Extraction Methods ◽  
Local Feature ◽  
Retinal Vessels ◽  
Feature Points ◽  
Fundus Image ◽  
Scale Invariant ◽  
Feature Based ◽  
Image Pairs

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).

scholarly journals On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Elisabeth K¨allstr¨om ◽  
Tomas Olsson ◽  
John Lindstr¨om ◽  
Lars Hakansson ◽  
Jonas Larsson
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Extraction Methods ◽  
Continuous Queries ◽  
Case Based Reasoning ◽  
Heavy Duty ◽  
Data Stream Management ◽  
Detection And Diagnosis ◽  
Case Based ◽  
Slippage Detection

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized.This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.

Early initiation of chemotherapy after histological diagnosis to improve the prognosis of metastatic pancreatic ductal adenocarcinoma.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 273-273 ◽  
Author(s):  
Rei Suzuki ◽  
Tadayuki Takagi ◽  
Takuto Hikichi ◽  
Ai Sato ◽  
Ko Watanabe ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Waiting Time ◽  
Early Initiation ◽  
Histological Diagnosis ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Kaplan Meier ◽  
Significant Difference ◽  
Detection And Diagnosis ◽  
Medical University

273 Background: Since pancreatic ductal adenocarcinoma (PDAC) shows aggressive progression, we speculate that prolonged waiting time after detection of disease until initiation of treatment may relate to prognosis of patients with advanced diseases. Methods: We included patients diagnosed to have metastatic PDAC in Fukushima Medical University between September 2006 and January 2014. All patients underwent more than 2 cycles of gemcitabine treatment (1,000 mg/m2 on days 1, 8, and 15 of a 28-day cycle) after confirming histological diagnosis. We classified waiting time as Time A (detection-to-diagnosis waiting time) and Time B (diagnosis-to-treatment waiting time). Each period was further divided into 2 groups (shorter [-short] or longer [-long] waiting time than median length of each waiting time group). Kaplan-Meier methods, log-rank test and Cox proportional hazard methods were used to analyze overall survival (OS). Results: Twenty three patients were included. Median age was 64 (49-75) and length of waiting time was 19.5 days (4-78) in Time A and 9.0 days (2-34) in Time B. Regarding Time A, there was no significant difference in median OS between Time A-short and Time A-long (198.5 vs. 197.0 days; hazard ration [HR] 1.096; 95% confidential interval [CI] 0.4822-2.537; P=0.81). On the other hand, median OS was significantly better in Time B-short than Time B-long (median OS 290 vs. 160 days; HR, 0.381; 95% CI, 0.096-0.622; P= 0.0078). Conclusions: We foundthat waiting time between disease detection and diagnosis didn’t impact on prognosis of metastatic PDAC contrary to our speculation. However, patients with short waiting time (less than 9 days) after diagnosis until initiation of first chemotherapy had better prognosis than patients who had long waiting time. These findings suggest that early initiation of chemotherapy after histological diagnosis can improve the prognosis of metastatic PDAC.

scholarly journals Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and Surrounding Vessels in CT Images Using Deep Convolutional Neural Networks and Texture Descriptors

2021 ◽  
Author(s):  
Tahereh Mahmoudi ◽  
Zahra Mousavi Kouzahkanan ◽  
Amir Reza Radmard ◽  
Raheleh Kafieh ◽  
Aneseh Salehnia ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Ct Images ◽  
Ductal Adenocarcinoma ◽  
Tumor Segmentation ◽  
Great Success ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Portal Venous Phase ◽  
3D Cnn

Abstract Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of Pancreatic Ductal Adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a new loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fin-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

scholarly journals MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 359 ◽  
Author(s):  
Omneya Attallah
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Extraction Methods ◽  
Textural Analysis ◽  
World Health ◽  
Histopathological Diagnosis ◽  
Efficient System ◽  
Time Frequency ◽  
Histopathological Images ◽  
Frequency Features

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists’ expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

scholarly journals Multi-Scale Geospatial Object Detection Based on Shallow-Deep Feature Extraction

2019 ◽  
Vol 11 (21) ◽  
pp. 2525 ◽  
Author(s):  
Dalal AL-Alimi ◽  
Yuxiang Shao ◽  
Ruyi Feng ◽  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Extraction Methods ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale ◽  
Deep Feature ◽  
Deep Feature Extraction

Multi-class detection in remote sensing images (RSIs) has garnered wide attention and introduced several service applications in many fields, including civil and military fields. However, several reasons make detection from aerial images very challenging and more difficult than nature scene images: Objects do not have a fixed size, often appear at very various scales and sometimes appear in dense groups, like vehicles and storage tanks, and have different surroundings or background areas. Furthermore, all of this makes the manual annotation of objects very complex and costly. The powerful effect of the feature extraction methods on object detection and the successes of deep convolutional neural networks (CNN) extract deep features more than traditional methods. This study introduced a novel network structure and designed a unique feature extraction which employs squeeze and excitation network (SENet) and residual network (ResNet) to obtain feature maps, named a shallow-deep feature extraction (SDFE), that improves the resolution and the localization at the same time. Furthermore, this novel model reduces the loss of dense groups and small objects, and provides higher and more stable detection accuracy which is not significantly affected by changing the value of the threshold of the intersection over union (IoU) and overcomes the difficulties of RSIs. Moreover, this study introduced strong evidence about the factors that affect the detection of RSIs. The proposed shallow-deep and multi-scale (SD-MS) method outperforms other approaches for the given ten classes of the NWPU VHR-10 dataset.

scholarly journals Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP)

2020 ◽  
Vol 9 (12) ◽  
pp. 4013
Author(s):  
Sebastian Ziegelmayer ◽  
Georgios Kaissis ◽  
Felix Harder ◽  
Friederike Jungmann ◽  
Tamara Müller ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Autoimmune Pancreatitis ◽  
Ct Scans ◽  
Ductal Adenocarcinoma ◽  
Feature Maps ◽  
Sensitivity Specificity

The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% (n = 18) to discriminate between AIP or PDAC. Feature maps were plotted and visual difference was noted. The machine learning (ML) model achieved a sensitivity, specificity, and ROC-AUC of 0.89 ± 0.11, 0.83 ± 0.06, and 0.90 ± 0.02 for the deep features and 0.72 ± 0.11, 0.78 ± 0.06, and 0.80 ± 0.01 for the radiomic features. Visualization of feature maps indicated different activation patterns for AIP and PDAC. We successfully trained a machine learning model using deep feature extraction from CT-images to differentiate between AIP and PDAC. In comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC. Visualization of deep features could further improve the diagnostic accuracy of non-invasive differentiation of AIP and PDAC.

Sign in / Sign up

Export Citation Format

Share Document