Understanding privacy risks in typical deep learning models for medical image analysis

2021 ◽  
Author(s):  
Nagesh Subbanna ◽  
Anup Tuladhar ◽  
Matthias Wilms ◽  
Nils-Daniel Forkert
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Learning Models ◽  
Privacy Risks

scholarly journals Medical Imaging using Deep Learning Models

2021 ◽  
Vol 6 (5) ◽  
pp. 156-167
Author(s):  
Chetanpal Singh
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Potential Role ◽  
Medical Image Analysis ◽  
Learning Models ◽  
Quality Healthcare ◽  
Learning Techniques

Deep learning has played a potential role in quality healthcare with fast automated and proper medical image analysis. In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's body. However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image analysis. It is necessary to state here that the deep learning approach is gaining attention in the medical imaging field in evaluating the presence or absence of disease in a patient. Mammography images, digital histopathology images, computerized tomography, etc. are some of the areas on which DL implementation focuses. One upon going through the paper will get to know the recent development that has occurred in this field and come up with a critical review on this aspect. The paper has demonstrated in detail modern deep learning models that are implemented in medical image analysis. There is no doubt about the promising future of the deep learning models and according to experts; the implementation of deep learning techniques has outperformed medical experts in numerous tasks. However, deep learning also has some drawbacks and challenges that are required to be addressed like limited datasets and many more. To mitigate such kinds of challenges, researchers are working on this aspect so that they can enhance healthcare by deploying AI.

Semantic Medical Image Analysis

2021 ◽  
pp. 128-146
Author(s):  
Joy Nkechinyere Olawuyi ◽  
Bernard Ijesunor Akhigbe ◽  
Babajide Samuel Afolabi ◽  
Attoh Okine
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Feature Representation ◽  
Practical Implementation ◽  
Learning Models ◽  
Training Models

The recent advancement in imaging technology, together with the hierarchical feature representation capability of deep learning models, has led to the popularization of deep learning models. Thus, research tends towards the use of deep neural networks as against the hand-crafted machine learning algorithms for solving computational problems involving medical images analysis. This limitation has led to the use of features extracted from non-medical data for training models for medical image analysis, considered optimal for practical implementation in clinical setting because medical images contain semantic contents that are different from that of natural images. Therefore, there is need for an alternative to cross-domain feature-learning. Hence, this chapter discusses the possible ways of harnessing domain-specific features which have semantic contents for development of deep learning models.

Deep Learning: A Breakthrough in Medical Imaging

2020 ◽  
Vol 16 (8) ◽  
pp. 946-956
Author(s):  
Hafiz Mughees Ahmad ◽  
Muhammad Jaleed Khan ◽  
Adeel Yousaf ◽  
Sajid Ghuffar ◽  
Khurram Khurshid
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Intelligent Systems ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Learning Models ◽  
Medical Systems ◽  
Quality Healthcare ◽  
Promising Solution

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.

scholarly journals Explainable Deep Learning Models in Medical Image Analysis

2020 ◽  
Vol 6 (6) ◽  
pp. 52 ◽  
Author(s):  
Amitojdeep Singh ◽  
Sourya Sengupta ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Black Box ◽  
Clinical Use ◽  
Learning Models ◽  
Literature Reviews ◽  
Medical Diagnostic ◽  
Practical Standpoint

Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those. However, the black-box nature of the algorithms has restricted their clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users.

scholarly journals pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis

2021 ◽  
Vol 198 ◽  
pp. 105796
Author(s):  
Alain Jungo ◽  
Olivier Scheidegger ◽  
Mauricio Reyes ◽  
Fabian Balsiger
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Data Handling ◽  
Python Package

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.

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