scholarly journals Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10982
Author(s):  
Lakpa Dorje Tamang ◽  
Byung Wook Kim
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Learning Approaches ◽  
Diagnostic Systems ◽  
Future Health ◽  
Preclinical Phase ◽  
Different Types ◽  
Future Health Care ◽  
Effective Diagnosis

Unprecedented breakthroughs in the development of graphical processing systems have led to great potential for deep learning (DL) algorithms in analyzing visual anatomy from high-resolution medical images. Recently, in digital pathology, the use of DL technologies has drawn a substantial amount of attention for use in the effective diagnosis of various cancer types, especially colorectal cancer (CRC), which is regarded as one of the dominant causes of cancer-related deaths worldwide. This review provides an in-depth perspective on recently published research articles on DL-based CRC diagnosis and prognosis. Overall, we provide a retrospective synopsis of simple image-processing-based and machine learning (ML)-based computer-aided diagnosis (CAD) systems, followed by a comprehensive appraisal of use cases with different types of state-of-the-art DL algorithms for detecting malignancies. We first list multiple standardized and publicly available CRC datasets from two imaging types: colonoscopy and histopathology. Secondly, we categorize the studies based on the different types of CRC detected (tumor tissue, microsatellite instability, and polyps), and we assess the data preprocessing steps and the adopted DL architectures before presenting the optimum diagnostic results. CRC diagnosis with DL algorithms is still in the preclinical phase, and therefore, we point out some open issues and provide some insights into the practicability and development of robust diagnostic systems in future health care and oncology.

scholarly journals Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 51
Author(s):  
Emanuela Paladini ◽  
Edoardo Vantaggiato ◽  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Texture Features ◽  
Tissue Type ◽  
Cancer Tissue ◽  
Learning Methods ◽  
Feature Based ◽  
Type Classification ◽  
Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

scholarly journals Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Tahsin Kurc ◽  
Spyridon Bakas ◽  
Xuhua Ren ◽  
Aditya Bagari ◽  
Alexandre Momeni ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Pathology ◽  
Learning Approaches ◽  
Research Challenges

Arrhythmia Diagnosis through ECG Signal Classification Using A Hybrid CNN-LSTM Model: Comparison with current Deep Learning Approaches (Preprint)

2020 ◽  
Author(s):  
Nicos Maglaveras ◽  
Georgios Petmezas ◽  
Vassilis Kilintzis ◽  
Leandros Stefanopoulos ◽  
Andreas Tzavelis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Model Comparison ◽  
Temporal Dynamics ◽  
Experimental Studies ◽  
Signal Classification ◽  
Ecg Signal ◽  
Learning Approaches ◽  
Different Types

BACKGROUND Electrocardiogram (ECG) recording and interpretation is the most common method used for the diagnosis of cardiac arrhythmias, nonetheless this process requires significant expertise and effort from the doctors’ perspective. Automated ECG signal classification could be a useful technique for the accurate detection and classification of several types of arrhythmias within a short timeframe. OBJECTIVE To review current approaches using state-of-the-art CNNs and deep learning methodologies in arrhythmia detection via ECG feature classification techniques and propose an optimised architecture capable of different types of arrhythmia diagnosis using publicly existing annotated arrhythmia databases from the MIT-BIH databases available at PHYSIONET (physionet.org) . METHODS A hybrid CNN-LSTM deep learning model is proposed to classify beats derived from two large ECG databases. The approach is proposed after a systematic review of current AI/DL methods applied in different types of arrhythmia diagnosis using the same public MIT-BIH databases. In the proposed architecture the CNN part carries out feature extraction and dimensionality reduction, and the LSTM part performs classification of the encoded ECG beat signals. RESULTS In experimental studies conducted with the MIT-BIH Arrhythmia and the MIT-BIH Atrial Fibrillation Databases average accuracies of 96.82% and 96.65% were noted respectively. CONCLUSIONS The proposed system can be used for arrhythmia diagnosis in clinical and mHealth applications managing a number of prevalent arrhythmias such as VT, AFIB, LBBB etc. The capability of CNNs to reduce the ECG beat signal’s size and extract its main features can be effectively combined with the LSTMs’ capability to learn the temporal dynamics of the input data for the accurate and automatic recognition of several types of cardiac arrhythmias. CLINICALTRIAL Not applicable.

scholarly journals xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1786
Author(s):  
Aurelia Bustos ◽  
Artemio Payá ◽  
Andrés Torrubia ◽  
Rodrigo Jover ◽  
Xavier Llor ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Microsatellite Instability ◽  
Digital Pathology ◽  
Model Performance ◽  
Tissue Microarrays ◽  
Learning System ◽  
Batch Effects ◽  
Patient Level ◽  
The Impact

The prediction of microsatellite instability (MSI) using deep learning (DL) techniques could have significant benefits, including reducing cost and increasing MSI testing of colorectal cancer (CRC) patients. Nonetheless, batch effects or systematic biases are not well characterized in digital histology models and lead to overoptimistic estimates of model performance. Methods to not only palliate but to directly abrogate biases are needed. We present a multiple bias rejecting DL system based on adversarial networks for the prediction of MSI in CRC from tissue microarrays (TMAs), trained and validated in 1788 patients from EPICOLON and HGUA. The system consists of an end-to-end image preprocessing module that tile samples at multiple magnifications and a tissue classification module linked to the bias-rejecting MSI predictor. We detected three biases associated with the learned representations of a baseline model: the project of origin of samples, the patient’s spot and the TMA glass where each spot was placed. The system was trained to directly avoid learning the batch effects of those variables. The learned features from the bias-ablated model achieved maximum discriminative power with respect to the task and minimal statistical mean dependence with the biases. The impact of different magnifications, types of tissues and the model performance at tile vs patient level is analyzed. The AUC at tile level, and including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 4 magnifications, was 0.87 ± 0.03 and increased to 0.9 ± 0.03 at patient level. To the best of our knowledge, this is the first work that incorporates a multiple bias ablation technique at the DL architecture in digital pathology, and the first using TMAs for the MSI prediction task.

scholarly journals Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

2022 ◽  
Vol 12 (1) ◽  
pp. 738
Author(s):  
Akella S. Narasimha Raju ◽  
Kayalvizhi Jayavel ◽  
Tulasi Rajalakshmi
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Premalignant Lesions ◽  
Computer Assisted ◽  
Screening Methods ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>

scholarly journals Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

2020 ◽  
Vol 10 (5) ◽  
pp. 1773 ◽  
Author(s):  
Hafiz Zia Ur Rehman ◽  
Hyunho Hwang ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Learning Approaches ◽  
Diagnostic Systems ◽  
Comprehensive Overview ◽  
Current Trends ◽  
Imaging Protocols ◽  
Skull Stripping ◽  
The Brain ◽  
Brain Parts

Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.

scholarly journals Question-driven summarization of answers to consumer health questions

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Max Savery ◽  
Asma Ben Abacha ◽  
Soumya Gayen ◽  
Dina Demner-Fushman
Keyword(s):  
Deep Learning ◽  
Health Information ◽  
Gold Standard ◽  
State Of The Art ◽  
Learning Approaches ◽  
Consumer Health ◽  
Automatic Summarization ◽  
Medical Expertise ◽  
Different Types

Abstract Automatic summarization of natural language is a widely studied area in computer science, one that is broadly applicable to anyone who needs to understand large quantities of information. In the medical domain, automatic summarization has the potential to make health information more accessible to people without medical expertise. However, to evaluate the quality of summaries generated by summarization algorithms, researchers first require gold standard, human generated summaries. Unfortunately there is no available data for the purpose of assessing summaries that help consumers of health information answer their questions. To address this issue, we present the MEDIQA-Answer Summarization dataset, the first dataset designed for question-driven, consumer-focused summarization. It contains 156 health questions asked by consumers, answers to these questions, and manually generated summaries of these answers. The dataset’s unique structure allows it to be used for at least eight different types of summarization evaluations. We also benchmark the performance of baseline and state-of-the-art deep learning approaches on the dataset, demonstrating how it can be used to evaluate automatically generated summaries.

A Review on the Integration of Deep Learning and Service-Oriented Architecture

2021 ◽  
Vol 32 (3) ◽  
pp. 95-119
Author(s):  
Marcelo Fantinato ◽  
Sarajane Marques Peres ◽  
Eleanna Kafeza ◽  
Dickson K. W. Chiu ◽  
Patrick C. K. Hung
Keyword(s):  
Deep Learning ◽  
Web Services ◽  
Service Oriented Architecture ◽  
Learning Technology ◽  
Learning Approaches ◽  
Current Trends ◽  
Different Types ◽  
Information And Communication ◽  
Service Oriented ◽  
Different Levels

In recent years, machine learning has been used for data processing and analysis, providing insights to businesses and policymakers. Deep learning technology is promising to further revolutionize this processing leading to better and more accurate results. Current trends in information and communication technology are accelerating widespread use of web services in supporting a service-oriented architecture (SOA) consisting of services, their compositions, interactions, and management. Deep learning approaches can be applied to support the development of SOA-based solutions, leveraging the vast amount of data on web services currently available. On the other hand, SOA has mechanisms that can support the development of distributed, flexible, and reusable infrastructures for the use of deep learning. This paper presents a literature survey and discusses how SOA can be enabled by as well as facilitate the use of deep learning approaches in different types of environments for different levels of users.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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