Interpretation of deep learning using attributions: application to ophthalmic diagnosis

Singh, Amitojdeep; Mohammed, Abdul Rasheed; Zelek, John; Lakshminarayanan, Vasudevan

doi:10.1117/12.2568631

Cited by 25 publications

(24 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparative analysis of various explainability models including DeepDeep Learning Important FeaTures (LIFT), DeepSHapley Additive exPlanations (SHAP), IG, etc. was performed for on a model for detection of choroidal neovascularization (CNV), diabetic macular edema (DME), and drusens from optical coherence tomography (OCT) scans [ 40 ]. Figure 4 highlights better localization achieved by newer methods (e.g., DeepSHAP) in contrast to noisy results from older methods (e.g., saliency maps).…”

Section: Applicationsmentioning

confidence: 99%

Explainable Deep Learning Models in Medical Image Analysis

2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Applicationsmentioning

confidence: 99%

Explainable Deep Learning Models in Medical Image Analysis

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, their predictions lack clinical interpretation, despite their high accuracy. This black box nature of DCNNs is the major problem that makes them unsuitable for clinical application [ 86 , 152 , 153 ] and has made the topic of eXplainable AI (XAI) of major importance [ 153 ]. Recently, visualization techniques such as gradient-based XAI have been widely used for evaluating networks.…”

Section: Resultsmentioning

confidence: 99%

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

et al. 2021

Self Cite

View full text Add to dashboard Cite

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.

show abstract

“…However, other areas of medicine, for example, ophthalmology, have shown that certain classifiers approach clinician-level performance. Of further importance is the development of explainable AI methods that have been applied to ophthalmology where correlations are made between areas of the image that the clinician uses to make decisions and the ones used by the algorithms to arrive at the result (i.e., the portions of the image that most heavily weigh the neural connections) [83,[225][226][227].…”

Section: Discussionmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

Self Cite

View full text Add to dashboard Cite

Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

show abstract

Interpretation of deep learning using attributions: application to ophthalmic diagnosis

Cited by 25 publications

References 0 publications

Explainable Deep Learning Models in Medical Image Analysis

Explainable Deep Learning Models in Medical Image Analysis

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

MR Images, Brain Lesions, and Deep Learning

Contact Info

Product

Resources

About