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Paper

Explainable Deep Learning Methods in Medical Image Classification: A Survey

by Independent / Community 001deace2f4b3db4ee6ed0f99873f1bfe852c118

Free2AITools Nexus Index

70.5

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 88

P: Popularity 65

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, lead...

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Semantic Scholar 145 Citations

Paper Information Summary
Entity Passport
Registry ID	001deace2f4b3db4ee6ed0f99873f1bfe852c118
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{001deace2f4b3db4ee6ed0f99873f1bfe852c118,
  author = {Unknown},
  title = {Explainable Deep Learning Methods in Medical Image Classification: A Survey Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/001deace2f4b3db4ee6ed0f99873f1bfe852c118}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Explainable Deep Learning Methods in Medical Image Classification: A Survey [Paper]. Free2AITools. https://api.semanticscholar.org/001deace2f4b3db4ee6ed0f99873f1bfe852c118

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 88

Popularity (P) 65

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Explainable Deep Learning Methods in Medical Image Classification: A Survey: Authority (A:88), Popularity (P:65), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, lead..."

❝ Cite Node

@article{Unknown2026Explainable,
  title={Explainable Deep Learning Methods in Medical Image Classification: A Survey},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈145CitationsSemantic Scholar

🏛️88AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 145

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