Birds look like cars: adversarial analysis of intrinsically interpretable deep learning
Sign Up to like & getrecommendations! Published in 2025 at "Machine Learning"
DOI: 10.1007/s10994-025-06896-w
Abstract: A common belief is that intrinsically interpretable deep learning models ensure a correct, intuitive understanding of their behavior and offer greater robustness against accidental errors or intentional manipulation. However, these beliefs have not been comprehensively… read more here.
Keywords: interpretable deep; deep learning; learning; intrinsically interpretable ... See more keywords
Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
Sign Up to like & getrecommendations! Published in 2021 at "iScience"
DOI: 10.1016/j.isci.2021.102373
Abstract: Summary Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become… read more here.
Keywords: diagnosis; deep learning; interpretable deep; automatic diagnosis ... See more keywords
IDL-PPBopt: A Strategy for Prediction and Optimization of Human Plasma Protein Binding of Compounds via an Interpretable Deep Learning Method
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00297
Abstract: The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting more… read more here.
Keywords: prediction optimization; idl ppbopt; deep learning; interpretable deep ... See more keywords
Uncovering the Dynamic Drivers of Floods Through Interpretable Deep Learning
Sign Up to like & getrecommendations! Published in 2024 at "Earth's Future"
DOI: 10.1029/2024ef004751
Abstract: The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black‐box nature of deep learning, its use alone does… read more here.
Keywords: deep learning; interpretable deep; uncovering dynamic; driving factors ... See more keywords
DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning
Sign Up to like & getrecommendations! Published in 2019 at "Scientific Reports"
DOI: 10.1038/s41598-019-38491-0
Abstract: Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health… read more here.
Keywords: ill patients; deep learning; critically ill; deepsofa ... See more keywords
An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-024-84749-7
Abstract: To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a… read more here.
Keywords: deep learning; interpretable deep; model nomogram; model ... See more keywords
Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac015
Abstract: Phosphorylation of proteins is one of the most significant post-translational modifications (PTMs) and plays a crucial role in plant functionality due to its impact on signaling, gene expression, enzyme kinetics, protein stability and interactions. Accurate… read more here.
Keywords: deep tabular; phosphorylation sites; tabular learning; sites soybean ... See more keywords
A Linguistically Interpretable Deep Fuzzy Classification System With Feature Transformation and Reconstruction
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2024.3394897
Abstract: Classification tasks involving tabular data often require a balance between exceptional performance and heightened interpretability. To address this challenge, we propose a linguistically interpretable deep fuzzy classification system called FFT-FFR-RBFC. The system employs a fuzzy… read more here.
Keywords: classification; interpretable deep; system; linguistically interpretable ... See more keywords
Stealthy Query-Efficient OpaqueAttack Against Interpretable Deep Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Reliability"
DOI: 10.1109/tr.2025.3551717
Abstract: Deep neural network (DNN) models are susceptible to adversarial samples in white-box and opaqueenvironments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security… read more here.
Keywords: interpretable deep; deep learning; stealthy query; attack ... See more keywords
Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound
Sign Up to like & getrecommendations! Published in 2025 at "BMC Medical Informatics and Decision Making"
DOI: 10.1186/s12911-025-03193-3
Abstract: This study aims to develop and validate an interpretable deep learning (DL) model and a nomogram based on endoscopic ultrasound (EUS) images for the prediction of pathological grading in pancreatic neuroendocrine tumors (PNETs). This multicenter… read more here.
Keywords: interpretable deep; deep learning; pathological grading; model nomogram ... See more keywords
Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network.
Sign Up to like & getrecommendations! Published in 2021 at "Optics express"
DOI: 10.1364/oe.411291
Abstract: Coherent imaging through scatter is a challenging task. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep learning approach can… read more here.
Keywords: deep neural; imaging scatter; interpretable deep; neural network ... See more keywords