Category: Medical ML Diagnosis

ML for Medical Imaging Diagnosis

Medical ML: Training Programs for Physicians — Building AI Competency in Medical Imaging

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752838  34stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The successful integration of artificial intelligence into clinical radiology practice hinges upon physicians' comprehensive understanding of AI principles, capabilities, and limitations. This research article examines the current landscape of physician training programs for AI in medical imaging, analyzing curriculum frameworks, competency standards, and pedagogical approaches across internati...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752838 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
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[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥60% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Quality Assurance and Monitoring for Medical AI Systems

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18709914  63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources57%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]3,414✓Minimum 2,000 words for a full research article. Current: 3,414
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18709914
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]46%✗≥60% of references from 2025–2026. Current: 46%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The deployment of machine learning algorithms in clinical diagnostics represents one of healthcare's most significant technological advances. However, unlike traditional medical devices, AI systems are uniquely susceptible to performance degradation through data drift, concept shift, and environmental changes that can compromise patient safety. This article presents a comprehensive framework fo...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18709914 63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources57%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI71%○≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]3,414✓Minimum 2,000 words for a full research article. Current: 3,414
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18709914
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]46%✗≥60% of references from 2025–2026. Current: 46%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Confidence Thresholds and Escalation Protocols in Clinical AI Deployment

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752845  81stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
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[i]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
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[w]Words [REQ]3,884✓Minimum 2,000 words for a full research article. Current: 3,884
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The deployment of artificial intelligence in medical imaging requires sophisticated mechanisms for determining when AI predictions should be trusted autonomously versus when human expert review is mandatory. This article presents a comprehensive framework for implementing confidence thresholds and escalation protocols in clinical AI systems, addressing the critical gap between algorithmic outpu...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752845 81stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]3,884✓Minimum 2,000 words for a full research article. Current: 3,884
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752845
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Medical ML: Radiologist-AI Collaboration Protocols – Designing Human-Machine Partnerships for Clinical Excellence

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Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18704558  81stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704558
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence into radiology practice represents more than a technological upgrade—it constitutes a fundamental reimagining of diagnostic workflows that have remained largely unchanged for decades. This article examines the critical protocols governing radiologist-AI collaboration, analyzing the spectrum of interaction models from autonomous AI triage to fully super...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18704558 81stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed100%✓≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References1 refs○Minimum 10 references required
[w]Words [REQ]3,759✓Minimum 2,000 words for a full research article. Current: 3,759
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18704558
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] PACS Integration Strategies for AI-Powered Medical Imaging: A Comprehensive Framework

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752847  64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources41%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef41%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]3,479✓Minimum 2,000 words for a full research article. Current: 3,479
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752847
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]19%✗≥60% of references from 2025–2026. Current: 19%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The integration of artificial intelligence (AI) algorithms into Picture Archiving and Communication Systems (PACS) represents a pivotal transformation in diagnostic radiology, enabling automated analysis, enhanced detection, and improved workflow efficiency. This comprehensive review examines the technical architectures, implementation strategies, and organizational considerations essential for...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752847 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources41%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI88%✓≥80% have a Digital Object Identifier
[b]CrossRef41%○≥80% indexed in CrossRef
[i]Indexed6%○≥80% have metadata indexed
[l]Academic88%✓≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]3,479✓Minimum 2,000 words for a full research article. Current: 3,479
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752847
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]19%✗≥60% of references from 2025–2026. Current: 19%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Federated Learning for Privacy-Preserving Medical AI Training: Multi-Institutional Collaboration Without Data Sharing

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18685263  73stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources75%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,839✓Minimum 2,000 words for a full research article. Current: 2,839
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685263
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (88 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Federated learning (FL) represents a paradigm shift in collaborative machine learning that enables multiple healthcare institutions to jointly train diagnostic AI models without sharing sensitive patient data. This comprehensive analysis examines the technical foundations, implementation strategies, and real-world deployments of federated learning in medical imaging, addressing the fundamental ...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18685263 73stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources75%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef75%○≥80% indexed in CrossRef
[i]Indexed25%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,839✓Minimum 2,000 words for a full research article. Current: 2,839
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18685263
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (88 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672185  68stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources58%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI95%✓≥80% have a Digital Object Identifier
[b]CrossRef79%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]3,242✓Minimum 2,000 words for a full research article. Current: 3,242
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672185
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥60% of references from 2025–2026. Current: 17%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (79 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). Transfer Learning and Domain Adaptation: Bridging the Data Gap in Medical Imaging AI. Medical ML Diagnosis Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18672185 Abstract The remarkable success of deep learning in medical imaging has been tempered by a fundamental challenge: the scarcity of large-scale, annotated medical datasets esse...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672185 68stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources58%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI95%✓≥80% have a Digital Object Identifier
[b]CrossRef79%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access32%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]3,242✓Minimum 2,000 words for a full research article. Current: 3,242
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18672185
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥60% of references from 2025–2026. Current: 17%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (79 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Explainable AI (XAI) for Clinical Trust: Bridging the Black Box Gap

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752850  70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed7%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]4,847✓Minimum 2,000 words for a full research article. Current: 4,847
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752850
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]7%✗≥60% of references from 2025–2026. Current: 7%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

of radiologists report they would not trust an AI diagnosis they cannot understand, even if the system demonstrates superior accuracy

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752850 70stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
[i]Indexed7%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]4,847✓Minimum 2,000 words for a full research article. Current: 4,847
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18752850
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]7%✗≥60% of references from 2025–2026. Current: 7%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (82 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Hybrid Models: Best of Both Worlds — CNN-Transformer Architectures for Clinical Imaging

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18752852  81stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed100%✓≥80% have metadata indexed
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The convergence of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represents a paradigm shift in medical image analysis, addressing the fundamental limitations of each architecture through strategic integration. This comprehensive review examines hybrid CNN-Transformer architectures that leverage CNNs' exceptional local feature extraction capabilities alongside Transformers...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18752852 81stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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[Medical ML] Vision Transformers in Radiology: Architecture, Applications, and Clinical Performance

Posted on by Yoman
Medical Research
Medical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672181  81stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

Vision Transformers (ViT) represent a paradigm shift in medical image analysis, applying the revolutionary attention mechanism from natural language processing to radiological imaging. This comprehensive review examines the theoretical foundations, architectural innovations, and clinical applications of Vision Transformers across radiology subspecialties including chest radiography, computed to...

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Medical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672181 81stabilfr·wdophcgmx
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Score = Ref Trust (91 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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