scholarly journals Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

2020 ◽  
Vol 10 (20) ◽  
pp. 7201
Author(s):  
Xiao-Xia Yin ◽  
Lihua Yin ◽  
Sillas Hadjiloucas
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Information Fusion ◽  
State Of The Art ◽  
General Structure ◽  
Building Blocks ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Dce Mri ◽  
Computer Aided

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

scholarly journals Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

2021 ◽  
Vol 11 (2) ◽  
pp. 760
Author(s):  
Yun-ji Kim ◽  
Hyun Chin Cho ◽  
Hyun-chong Cho
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Medical Data ◽  
Classification Model ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Original Training ◽  
Computer Aided ◽  
Aided Diagnosis

Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals Interpretable Retrosynthesis Prediction in Two Steps

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
Yang Yu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Building Blocks ◽  
Computer Aided ◽  
Free Model ◽  
Benchmark Datasets ◽  
Previous State ◽  
Target Molecules ◽  
Template Free ◽  
Synthetic Routes ◽  
High Diversity

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and lacks interpretability behind their predictions. In this study, we devise a novel template-free model for retrosynthetic expansion by automating the procedure that chemistsusedtodo. Our method plans synthesis in two steps, by first identifying the potential disconnection bonds of the molecule graph with a graph neural network and thereafter generating synthons according to the identified disconnection bonds of the target molecule graph, and then predicting the associated reactants SMILES based on the obtained synthons with a reactant prediction model. While outperforming previous state-of-the-art baselines by a significant margin on the benchmark datasets, our model also provides predictions with high diversity and chemically reasonable interpretation.

scholarly journals Interpretable Retrosynthesis Prediction in Two Steps

2020 ◽  
Author(s):  
Chaochao Yan ◽  
Qianggang Ding ◽  
Peilin Zhao ◽  
Shuangjia Zheng ◽  
Jinyu Yang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Building Blocks ◽  
Computer Aided ◽  
Free Model ◽  
Benchmark Datasets ◽  
Previous State ◽  
Target Molecules ◽  
Template Free ◽  
Synthetic Routes ◽  
High Diversity

Retrosynthesis is the process of recursively decomposing target molecules into available building blocks. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and lacks interpretability behind their predictions. In this study, we devise a novel template-free model for retrosynthetic expansion by automating the procedure that chemistsusedtodo. Our method plans synthesis in two steps, by first identifying the potential disconnection bonds of the molecule graph with a graph neural network and thereafter generating synthons according to the identified disconnection bonds of the target molecule graph, and then predicting the associated reactants SMILES based on the obtained synthons with a reactant prediction model. While outperforming previous state-of-the-art baselines by a significant margin on the benchmark datasets, our model also provides predictions with high diversity and chemically reasonable interpretation.

scholarly journals Artificial intelligence in architecture: Generating conceptual design via deep learning

2018 ◽  
Vol 16 (4) ◽  
pp. 306-327 ◽  
Author(s):  
Imdat As ◽  
Siddharth Pal ◽  
Prithwish Basu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Field Research ◽  
Building Blocks ◽  
Learning System ◽  
Generative Adversarial Networks

Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph-based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.

scholarly journals Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 2488 ◽  
Author(s):  
Muhammad Naseer Bajwa ◽  
Kaoru Muta ◽  
Muhammad Imran Malik ◽  
Shoaib Ahmed Siddiqui ◽  
Stephan Alexander Braun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Skin Diseases ◽  
State Of The Art ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD). This study aims at extending previous works on CAD for dermatology by exploring the potential of Deep Learning to classify hundreds of skin diseases, improving classification performance, and utilizing disease taxonomy. We trained state-of-the-art Deep Neural Networks on two of the largest publicly available skin image datasets, namely DermNet and ISIC Archive, and also leveraged disease taxonomy, where available, to improve classification performance of these models. On DermNet we establish new state-of-the-art with 80% accuracy and 98% Area Under the Curve (AUC) for classification of 23 diseases. We also set precedence for classifying all 622 unique sub-classes in this dataset and achieved 67% accuracy and 98% AUC. On ISIC Archive we classified all 7 diseases with 93% average accuracy and 99% AUC. This study shows that Deep Learning has great potential to classify a vast array of skin diseases with near-human accuracy and far better reproducibility. It can have a promising role in practical real-time skin disease diagnosis by assisting physicians in large-scale screening using clinical or dermoscopic images.

scholarly journals A Comparison of Deep Learning and Pharmacokinetic Model Selection Methods in Segmentation of High-Grade Glioma

2021 ◽  
Author(s):  
Azimeh NV Dehkordi ◽  
Sedigheh Sina ◽  
Freshteh Khodadadi
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Clinical Decision Making ◽  
Image Feature ◽  
Pharmacokinetic Parameters ◽  
Tumor Segmentation ◽  
Jaccard Coefficient ◽  
Dce Mri ◽  
Segmentation Accuracy ◽  
Tumor Region

Purpose: Glioma tumor segmentation is an essential step in clinical decision making. Recently, computer-aided methods have been widely used for rapid and accurate delineation of the tumor regions. Methods based on image feature extraction can be used as fast methods, while segmentation based on the physiology and pharmacokinetic of the tissues is more accurate. This study aims to compare the performance of tumor segmentation based on these two different methods. Materials and Methods: Nested Model Selection (NMS) based on Extended-Toft’s model was applied to 190 Dynamic Contrast-Enhanced MRI (DCE-MRI) slices acquired from 25 Glioblastoma Multiforme (GBM) patients in 70 time-points. A model with three pharmacokinetic parameters, Model 3, is usually assigned to tumor voxel based on the time-contrast concentration signal. We utilized Deep-Net as a CNN network, based on Deeplabv3+ and layers of pre-trained resnet18, which has been trained with 17288 T1-Contrast MRI slices with HGG brain tumor to predict the tumor region in our 190 DCE MRI T1 images. The NMS-based physiological tumor segmentation was considered as a reference to compare the results of tumor segmentation by Deep-Net. Dice, Jaccard, and overlay similarity coefficients were used to evaluate the tumor segmentation accuracy and reliability of the Deep tumor segmentation method. Results: The results showed a relatively high similarity coefficient (Dice coefficient: 0.73±0.15, Jaccard coefficient: 0.66±0.17, and overlay coefficient: 0.71±0.15) between deep learning tumor segmentation and the tumor region identified by the NMS method. The results indicate that the deep learning methods may be used as accurate and robust tumor segmentation. Conclusion: Deep learning-based segmentation can play a significant role to increase the segmentation accuracy in clinical application, if their training process is completely automatic and independent from human error.

A Survey on Machine Learning and Deep Learning based Computer Aided Methods for Detection of Polyps in CT Colonography

2020 ◽  
Vol 16 ◽  
Author(s):  
Niharika Hegde ◽  
Shishir M. ◽  
Shashank S. ◽  
Dayananda P. ◽  
Mrityunjaya V. Latte
Keyword(s):  
Machine Learning ◽  
Colon Cancer ◽  
Deep Learning ◽  
Early Detection ◽  
Ct Colonography ◽  
State Of The Art ◽  
Colon Polyps ◽  
Colon Wall ◽  
Computer Aided ◽  
Over Time

Background: Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over time, they can evolve into colon cancer, which when diagnosed in later stages is often fatal. Hence, time is of the essence in the early detection of polyps and the prevention of colon cancer. Methods: To aid this endeavour, many computer-aided methods have been developed, which use a wide array of techniques to detect, localize and segment polyps from CT Colonography images. In this paper, a comprehensive state-of-the-art method is proposed and categorize this work broadly using the available classification techniques using Machine Learning and Deep Learning. Conclusions: The performance of each of the proposed approach is analyzed with existing methods and also how they can be used to tackle the timely and accurate detection of colon polyps.

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