ArticleStat2020
Masked convolutional neural network for supervised learning problems.
Article in Stat, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Applications of interpretable deep learning in neuroimaging: A comprehensive review.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact.Computer methods in applied mechanics and engineering · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Convolutional neural networks (CNNs) have exhibited superior performance in various types of classification and prediction tasks, but their interpretability remains to be low despite years of research effort. It is crucial to improve the ability of existing models to interpret deep neural networks from both theoretical and practical perspectives and to develop new neural network models with interpretable representations. The aim of this paper is to propose a set of novel masked CNN (MCNN) models with better ability to interpret networks and more accurate prediction. The key ideas behind MCNNs are to introduce a latent binary network to extract informative regions of interest that contain important signals for prediction and to integrate the latent binary network with CNNs to achieve better prediction in various supervised learning problems. Extensive numerical studies demonstrate the competitive performance of the proposed MCNN models.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.