scholarly journals State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5546
Author(s):  
Saqib Ali ◽  
Jianqiang Li ◽  
Yan Pei ◽  
Rooha Khurram ◽  
Khalil ur Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Diagnosis ◽  
State Of The Art ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Critical Survey ◽  
Survey Analysis ◽  
Extensive Discussion ◽  
Future Challenges ◽  
Objective Description

Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.

scholarly journals Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1863
Author(s):  
Naira Elazab ◽  
Hassan Soliman ◽  
Shaker El-Sappagh ◽  
S. M. Riazul Islam ◽  
Mohammed Elmogy
Keyword(s):  
Deep Learning ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Disease Diagnosis ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Histological Image ◽  
Learning Techniques ◽  
Histopathology Images

Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.

scholarly journals State-of-the-art imaging for diagnosis of metastatic bone disease

Der Radiologe ◽  
2020 ◽  
Vol 60 (S1) ◽  
pp. 1-16 ◽  
Author(s):  
Amanda Isaac ◽  
Danoob Dalili ◽  
Daniel Dalili ◽  
Marc-André Weber
Keyword(s):  
Health Care ◽  
Bone Disease ◽  
State Of The Art ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Tumor Biology ◽  
Metastatic Bone Disease ◽  
Care Utilization ◽  
Care Plans ◽  
The Impact

AbstractMetastatic bone disease (MBD) is common—it is detected in up to 65–75% of patients with breast or prostate cancer, in over 35% of patients with lung cancer; and almost all patients with symptomatic multiple myeloma have focal lesions or a diffuse bone marrow infiltration. Metastatic bone disease can cause a variety of symptoms and is often associated with a poorer prognosis, with high social and health-care costs. Population-based cohort studies confirm significantly increased health-care utilization costs in patients presenting with cancer with MBD compared with those without MBD. The prolonged survival of patients with bone metastasis thanks to advances in therapy presents an opportunity for better treatments for this patient cohort. Early and accurate diagnosis of bone metastases is therefore crucial. The patterns and presentation of MBD are quite heterogeneous and necessitate good knowledge of the possibilities and limitations of each imaging modality. Here, we review the state-of-the-art imaging techniques, assess the need for evidence-based and cost-effective patient care pathways, and advocate multidisciplinary management based on collaborations between orthopedic surgeons, pathologists, oncologists, radiotherapists, and radiologists aimed at improving patient outcomes. Radiologists play a key role in this multidisciplinary approach to decision-making through correlating the tumor entity, the tumor biology, the impact on the surrounding tissues and progression, as well as the overall condition of the patient. This approach helps to choose the best patient-tailored imaging plan advocating a “choose wisely” strategy throughout the initial diagnosis, minimally invasive treatment procedures, as well as follow-up care plans.

Performance Analysis of Various Nanocontrast Agents and CAD Systems for Cancer Diagnosis

2019 ◽  
Vol 15 (9) ◽  
pp. 831-852
Author(s):  
Ruba Thanapandiyaraj ◽  
Tamilselvi Rajendran ◽  
Parisa Beham Mohammedgani
Keyword(s):  
Contrast Agents ◽  
Cancer Detection ◽  
Cancer Diagnosis ◽  
Imaging Modality ◽  
Imaging Techniques ◽  
Mri Contrast Agents ◽  
The Body ◽  
Cell Detection ◽  
Cad Systems ◽  
Different Types

Background: Cancer is a disease which involves the abnormal cell growth that has the potential of dispersal to other parts of the body. Among various conventional anatomical imaging techniques for cancer diagnosis, Magnetic Resonance Imaging (MRI) provides the best spatial resolution and is noninvasive. Current efforts are directed at enhancing the capabilities of MRI in oncology by adding contrast agents. Discussion: Recently, the superior properties of nanomaterials (extremely smaller in size, good biocompatibility and ease in chemical modification) allow its application as a contrast agent for early and specific cancer detection through the MRI. The precise detection of cancer region from any imaging modality will lead to a thriving treatment for cancer patients. The better localization of radiation dose can be attained from MRI by using suitable image processing algorithms. As there are many works that have been proposed for automatic detection for cancers, the effort is also put in to provide an effective survey of Computer Aided Diagnosis (CAD) system for different types of cancer detection with increased efficiency based on recent research works. Even though there are many surveys on MRI contrast agents, they only focused on a particular type of cancer. This study deeply presents the use of nanocontrast agents in MRI for different types of cancer diagnosis. Conclusion: The main aim of this paper is to critically review the available compounds used as nanocontrast agents in MRI modality for different types of cancers. It also includes the review of different methods for cancer cell detection and classification. A comparative analysis is performed to analyze the effect of different CAD systems.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Trends in Nanotechnology for in vivo Cancer Diagnosis: Products and Patents

2020 ◽  
Vol 26 (18) ◽  
pp. 2167-2181
Author(s):  
Tatielle do Nascimento ◽  
Melanie Tavares ◽  
Mariana S.S.B. Monteiro ◽  
Ralph Santos-Oliveira ◽  
Adriane R. Todeschini ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Quantum Dots ◽  
Magnetic Resonance ◽  
Metal Nanoparticles ◽  
Cancer Diagnosis ◽  
Imaging Techniques ◽  
Tumor Detection ◽  
Abnormal Growth ◽  
Pharmaceutical Nanotechnology

Background: Cancer is a set of diseases formed by abnormal growth of cells leading to the formation of the tumor. The diagnosis can be made through symptoms’ evaluation or imaging tests, however, the techniques are limited and the tumor detection may be late. Thus, pharmaceutical nanotechnology has emerged to optimize the cancer diagnosis through nanostructured contrast agent’s development. Objective: This review aims to identify commercialized nanomedicines and patents for cancer diagnosis. Methods: The databases used for scientific articles research were Pubmed, Science Direct, Scielo and Lilacs. Research on companies’ websites and articles for the recognition of commercial nanomedicines was performed. The Derwent tool was applied for patent research. Results: This article aimed to research on nanosystems based on nanoparticles, dendrimers, liposomes, composites and quantum dots, associated to imaging techniques. Commercialized products based on metal and composite nanoparticles, associated with magnetic resonance and computed tomography, have been observed. The research conducted through Derwent tool displayed a small number of patents using nanotechnology for cancer diagnosis. Among these patents, the most significant number was related to the use of systems based on metal nanoparticles, composites and quantum dots. Conclusion: Although few systems are found in the market and patented, nanotechnology appears as a promising field for the development of new nanosystems in order to optimize and accelerate the cancer diagnosis.

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

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