[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Age-Related Eye Disease Study 2 (特集 臨床において必要なサプリメントの知識)

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

Explainable Deep Learning Models in Medical Image Analysis.

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Applications of deep learning in fundus images: A review.

Ophthalmic diagnosis using deep learning with fundus images - A critical review.

An approach to accurately recognize various Yoga asanas using deep learning algorithms has been presented in this work. A dataset of six Yoga asanas (i.e. Bhujangasana, Padmasana, Shavasana, Tadasana, Trikonasana, and Vrikshasana) has been created using 15 individuals (ten males and five females) with a normal RGB webcam and is made publicly available. A hybrid deep learning model is proposed using convolutional neural network (CNN) and long short-term memory (LSTM) for Yoga recognition on real-time videos, where CNN layer is used to extract features from keypoints of each frame obtained from OpenPose and is followed by LSTM to give temporal predictions. To the best of our knowledge, this is the first study using an end-to-end deep learning pipeline to detect Yoga from videos. The system achieves a test accuracy of 99.04% on single frames and 99.38% accuracy after polling of predictions on 45 frames of the videos. Using a model with temporal data leverages the information from previous frames to give an accurate and robust result. We have also tested the system in real time for a different set of 12 persons (five males and seven females) and achieved 98.92% accuracy. Experimental results provide a qualitative assessment of the method as well as a comparison to the state-of-the-art.

Real-time Yoga recognition using deep learning

An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.

Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review

Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted 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.

Amitojdeep Singh

Papers

Explainable Deep Learning Models in Medical Image Analysis.

Ophthalmic diagnosis using deep learning with fundus images - A critical review.

Real-time Yoga recognition using deep learning

Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review

Explainable deep learning models in medical image analysis