Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroups Article Swipe
Lucy Godson
,
Navid Alemi
,
Jérémie Nsengimana
,
Graham P. Cook
,
Emily L. Clarke
,
Darren Treanor
,
D. Timothy Bishop
,
Julia Newton‐Bishop
,
Ali Gooya
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.11524
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.11524
Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential treatment strategies. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here we attempt to overcome this by developing deep learning models to classify gigapixel H&E stained pathology slides, which are well established in clinical workflows, into these immune subgroups. Previous subtyping approaches have employed supervised learning which requires fully annotated data, or have only examined single genetic mutations in melanoma patients. We leverage a multiple-instance learning approach, which only requires slide-level labels and uses an attention mechanism to highlight regions of high importance to the classification. Moreover, we show that pathology-specific self-supervised models generate better representations compared to pathology-agnostic models for improving our model performance, achieving a mean AUC of 0.76 for classifying histopathology images as high or low immune subgroups. We anticipate that this method may allow us to find new biomarkers of high importance and could act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2202.11524
- https://arxiv.org/pdf/2202.11524
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4221167633Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2202.11524Digital Object Identifier
- Title
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Weakly-supervised learning for image-based classification of primary melanomas into genomic immune subgroupsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-02-23Full publication date if available
- Authors
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Lucy Godson, Navid Alemi, Jérémie Nsengimana, Graham P. Cook, Emily L. Clarke, Darren Treanor, D. Timothy Bishop, Julia Newton‐Bishop, Ali GooyaList of authors in order
- Landing page
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https://arxiv.org/abs/2202.11524Publisher landing page
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https://arxiv.org/pdf/2202.11524Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2202.11524Direct OA link when available
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Workflow, Artificial intelligence, Machine learning, Computer science, Subtyping, Leverage (statistics), Digital pathology, Bioinformatics, Biology, Programming language, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.achieving | 161 |
| abstract_inverted_index.acquiring | 46 |
| abstract_inverted_index.annotated | 105 |
| abstract_inverted_index.approach, | 122 |
| abstract_inverted_index.attention | 131 |
| abstract_inverted_index.effective | 8 |
| abstract_inverted_index.expensive | 216 |
| abstract_inverted_index.gigapixel | 79 |
| abstract_inverted_index.highlight | 134 |
| abstract_inverted_index.improving | 15, 157 |
| abstract_inverted_index.landscape | 204 |
| abstract_inverted_index.mechanism | 132 |
| abstract_inverted_index.mutations | 113 |
| abstract_inverted_index.pathology | 82 |
| abstract_inverted_index.patients, | 209 |
| abstract_inverted_index.patients. | 19, 116 |
| abstract_inverted_index.potential | 42 |
| abstract_inverted_index.routinely | 59 |
| abstract_inverted_index.subtyping | 96 |
| abstract_inverted_index.treatment | 9, 43 |
| abstract_inverted_index.workflow. | 65 |
| abstract_inverted_index.additional | 215 |
| abstract_inverted_index.anticipate | 178 |
| abstract_inverted_index.approaches | 97 |
| abstract_inverted_index.associated | 35 |
| abstract_inverted_index.biomarkers | 188 |
| abstract_inverted_index.challenges | 13 |
| abstract_inverted_index.clinicians | 199 |
| abstract_inverted_index.developing | 73 |
| abstract_inverted_index.importance | 138, 191 |
| abstract_inverted_index.prognostic | 2 |
| abstract_inverted_index.subgroups, | 32 |
| abstract_inverted_index.subgroups. | 94, 176 |
| abstract_inverted_index.supervised | 100 |
| abstract_inverted_index.workflows, | 90 |
| abstract_inverted_index.Determining | 0 |
| abstract_inverted_index.classifying | 168 |
| abstract_inverted_index.early-stage | 1 |
| abstract_inverted_index.established | 87 |
| abstract_inverted_index.slide-level | 126 |
| abstract_inverted_index.strategies. | 44 |
| abstract_inverted_index.stratifying | 5 |
| abstract_inverted_index.differential | 37 |
| abstract_inverted_index.performance, | 160 |
| abstract_inverted_index.transcriptome | 25, 47 |
| abstract_inverted_index.histopathology | 169 |
| abstract_inverted_index.time-consuming | 51 |
| abstract_inverted_index.classification. | 141 |
| abstract_inverted_index.representations | 151 |
| abstract_inverted_index.self-supervised | 147 |
| abstract_inverted_index.multiple-instance | 120 |
| abstract_inverted_index.pathology-agnostic | 154 |
| abstract_inverted_index.pathology-specific | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |