: Towards Secure and Lightweight Deep Learning as a Medical Diagnostic Service

2021 ◽  
pp. 519-541
Author(s):  
Xiaoning Liu ◽  
Yifeng Zheng ◽  
Xingliang Yuan ◽  
Xun Yi
Keyword(s):  
Deep Learning ◽  
Diagnostic Service ◽  
Medical Diagnostic

Deep Learning In Medical Imaging And Drug Design

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Surayya Ado Bala ◽  
Shri Ojha Kant ◽  
Adamu Garba Yakasai
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Drug Design ◽  
Therapy Monitoring ◽  
Surrogate Endpoint ◽  
Cellular Level ◽  
Response To Therapy ◽  
Response To Treatment ◽  
Design Decision ◽  
Medical Diagnostic

Over the last decade, deep learning (DL) methods have been extremely successful and widely used in almost every domain. Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy. DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data. Medical imaging has transformed healthcare science, it was thought of as a diagnostic tool for disease, but now it is also used in drug design. Advances in medical imaging technology have enabled scientists to detect events at the cellular level. The role of medical imaging in drug design includes identification of likely responders, detection, diagnosis, evaluation, therapy monitoring, and follow-up. A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making. For this, a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment. The result is a quantifiable improvement in healthcare quality in most therapeutic areas, resulting in improvements in quality and duration of life. This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design. We briefly discuss the fields related to the history of deep learning, medical imaging, and drug design.

scholarly journals The usage of deep learning algorithm in medical diagnostic of breast cancer

2019 ◽  
Vol 15 (2) ◽  
pp. 274-281
Author(s):  
Arli Aditya Parikesit ◽  
Kevin Nathanael Ramanto
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Medical Diagnostics ◽  
Specific Gene ◽  
Deep Learning Algorithm ◽  
Medical Diagnostic ◽  
Visual Mechanism ◽  
Gene Status ◽  
Human Visual Mechanism

Diagnosis is a crucial step to identify the disease that experienced by the patient. Diagnosis includes information gathering, integration, and interpretation. However, diagnosis process is not an easy task. Diagnostic accuracy is depending on the experience and cognitive ability of diagnosticians. The new algorithm called deep learning that is developed by simulating the human visual mechanism has been implemented in medical diagnostics. One of the diseases that can be diagnosed by using deep learning algorithm is the breast cancer. Several studies showed that deep learning algorithm can be used for detecting and classifying lesions, detecting mitosis, and predicting specific gene status.  In this review article, 16 research journals were reviewed and discussed. The limitations of each algorithm are provided. All of the journals showed that deep learning algorithm has high diagnostics accuracy in assisting the professional diagnosticians to determine diagnosis outcome accordingly.

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.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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