bibliography
Science Score: 31.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (4.0%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: NASBIP8
- Language: TeX
- Default Branch: main
- Size: 81.1 KB
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Metadata Files
README.md
Autobibtex generator
Creating a .bib file from a folder structure involves scanning the folder for documents, extracting relevant information such as titles, authors, publication dates, and then formatting this information according to the BibTeX format from anothers .bib files. This process can be automated with a script that parses the documents, retrieves the necessary data, and outputs it in the correct syntax for a .bib file, ready to be used with LaTeX documents for citation management. It's a handy tool for researchers and students who deal with numerous references in their academic work.
Problemas:
Fixed BCC con XAI -> chanda2024 -> authors: hay un caracter que esta mal y no le gusta a python, quizas habria que acortar los autores y quedarse solo con 4 o asi
Owner
- Login: NASBIP8
- Kind: user
- Repositories: 1
- Profile: https://github.com/NASBIP8
Citation (Citations/BCC con XAI/Barata_RNN_XAI.bib)
@article{Barata_RNN_XAI,
title = {Explainable skin lesion diagnosis using taxonomies},
journal = {Pattern Recognition},
volume = {110},
pages = {107413},
year = {2021},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2020.107413},
url = {https://www.sciencedirect.com/science/article/pii/S0031320320302168},
author = {Catarina Barata and M. Emre Celebi and Jorge S. Marques},
keywords = {Hierarchical deep learning, Explainability, Channel attention, Spatial attention, Safety-critical CADS, Skin cancer},
abstract = {Deep neural networks have rapidly become an indispensable tool in many classification applications. However, the inclusion of deep learning methods in medical diagnostic systems has come at the cost of diminishing their explainability. This significantly reduces the safety of a diagnostic system, since the physician is unable to interpret and validate the output. Therefore, in this work we aim to address this major limitation and improve the explainability of a skin cancer diagnostic system. We propose to leverage two sources of information: (i) medical knowledge, in particular the taxonomic organization of skin lesions, which will be used to develop a hierarchical neural network; and (ii) recent advances in channel and spatial attention modules, which can identify interpretable features and regions in dermoscopy images. We demonstrate that the proposed approach achieves competitive results in two dermoscopy data sets (ISIC 2017 and 2018) and provides insightful information about its decisions, thus increasing the safety of the model.}
}
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