Federated Learning: Issues in Medical Application

29 de May de 2022 9 de January de 2022

In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.

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Highly accurate protein structure prediction with AlphaFold

24 de October de 2021 27 de August de 2021

Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

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R0:daa46e0033d013818bcc056d269d5737-Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets

Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets

2 de January de 2021 2 de January de 2021

“We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation”.

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COCIR analyses application of medical device legislation to Artificial Intelligence

5 de September de 2020 10 de November de 2020

“The European Commission has shown its ambition in the area of artificial intelligence (AI) in its recent White Paper on Artificial Intelligence – a European approach to excellence and trust. This White Paper is at the same time a precursor of possible legislation of AI in products and services in the European Union. However, COCIR sees no need for novel regulatory frameworks for AI-based devices in Healthcare, because the requirements of EU MDR and EU IVDR in combination with GDPR are adequate to ensure that same excellence and trust.” (COCIR paper).

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Artificial Intelligence in Medical Imaging

1 de August de 2020 10 de November de 2020

“This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging”.

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Transforming Health Care Through AI Revolutions

17 de July de 2020 10 de November de 2020

I will discuss relevant AI thrusts at NIST on health care informatics, focusing on the use of machine learning, knowledge representation and natural language processing. I will also discuss the need for explanations in AI systems (XAI) and current state of the art in medical XAI.

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DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

21 de June de 2020 21 de June de 2020

To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other.

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Machine Learning for Medical Imaging Analysis Demystified

4 de June de 2020 10 de November de 2020

This lecture will outline the fundamental ML processes involved in medical image analysis. Achieving prediction and classification for CAD applications will also be discussed. Some preliminary ideas of 3D reconstruction and viewing as applied in medical image analysis will also be presented.

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Guideline for AI for medical products

3 de June de 2020 3 de June de 2020

The objective of this guideline is to provide medical device manufacturers and notified bodies instructions and to provide them with a concrete checklist to understand what the expectations of the notified bodies are, to promote step-by-step implementation of safety of medical devices, that implement artificial intelligence methods, in particular machine learning, to compensate for the lack of a harmonized standard (in the interim) to the greatest extent possible.

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https://editorialia.com/wp-content/uploads/2020/05/a-human-centered-evaluation-of-a-deep-learning-system-deployed-in-clinics-for-the-detection-of-diabetic-retinopathy.jpg

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

27 de May de 2020 10 de November de 2020

This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.

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