[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.

Proceedings of the fifth annual workshop on Computational learning theory

We use some fractal analysis methods to study river flow fluctuations. The result of the Multifractal Detrended Fluctuation Analysis (MF-DFA) shows that there are two crossover timescales at s 1 × ∼ 12 and s 2 × ∼ 130 months in the fluctuation function. We discuss how the existence of the crossover timescales are related to a sinusoidal trend. The first crossover is due to the seasonal trend and the value of second one is approximately equal to the well-known cycle of sun activity. Using Fourier Detrended Fluctuation Analysis, the sinusoidal trend is eliminated. The values of Hurst exponents of the runoff water of rivers without the sinusoidal trend show a long-range correlation behavior. For the Daugava river, the value of Hurst exponent is 0.52 ± 0.01 and also we find that these fluctuations have multifractal nature. Comparing the MF-DFA results for the remaining data set of Daugava river to those for shuffled and surrogate series, we conclude that its multifractal nature is almost entirely due to the broadness of probability density function.

Fractal Analysis of River Flow Fluctuations

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

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.

Explainable deep learning models in medical image analysis

Deep learning methods for ophthalmic diagnosis have shown success for tasks like segmentation and classification but their implementation in the clinical setting is limited by the black-box nature of the algorithms. Very few studies have explored the explainability of deep learning in this domain. Attribution methods explain the decisions by assigning a relevance score to each input feature. Here, we present a comparative analysis of multiple attribution methods to explain the decisions of a convolutional neural network (CNN) in retinal disease classification from OCT images. This is the first such study to perform both quantitative and qualitative analyses. The former was performed using robustness, runtime, and sensitivity while the latter was done by a panel of eye care clinicians who rated the methods based on their correlation with diagnostic features. The study emphasizes the need for developing explainable models that address the end-user requirements, hence increasing the clinical acceptance of deep learning.

Sourya Sengupta

Papers

Explainable Deep Learning Models in Medical Image Analysis.

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

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

Explainable deep learning models in medical image analysis

What is the Optimal Attribution Method for Explainable Ophthalmic Disease Classification