ArticleInterpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data : 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, He...2021
Lattice Paths for Persistent Diagrams.
Article in Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data : 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, He..., 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 0 citations in OpenAlex.
- A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification.Scientific reports · 2025Article
- Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification.Journal of Cancer · 2025Article
- Topological analysis of COVID-19 wave patterns and policy responses in Europe.Frontiers in public health · 2025Article
- Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia.Artificial intelligence in medicine · 2023Article
- TOPOLOGICAL LEARNING FOR BRAIN NETWORKS.The annals of applied statistics · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Persistent homology has undergone significant development in recent years. However, one outstanding challenge is to build a coherent statistical inference procedure on persistent diagrams. In this paper, we first present a new lattice path representation for persistent diagrams. We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations. The lattice path method is applied to the topological characterization of the protein structures of the COVID-19 virus. We demonstrate that there are topological changes during the conformational change of spike proteins.
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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.