One label is all you need: Interpretable AI-enhanced histopathology for oncology
Open accessAbstract
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.
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Region-of-interest (ROI)-level annotations remain essential for localization and segmentation tasks, including tumor boundary identification and mitotic figure detection.
“In contrast, region-of-interest (ROI)-level annotations remain essential for localization and segmentation tasks, including tumor boundary identification and mitotic figure detection [46,47].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [47]
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Cross-Attention-Based Saliency Inference for Predicting Cancer Metastasis on Whole Slide Images
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Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature
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“In contrast, region-of-interest (ROI)-level annotations remain essential for localization and segmentation tasks, including tumor boundary identification and mitotic figure detection [46,47].”
- Authors
- Tavolara, T. E., Su, Z., Gurcan, M. N., & Niazi, M. K. K.
- Affiliations
- Wake Forest University Health Sciences (US)
- Corresponding author
- Niazi, M. Khalid Khan
- Venue
- Seminars in Cancer Biology
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 97
- Pages
- 70-85
- ISSN
- 1044-579X (ISSN-L)
- OpenAlex
- W4387521690
- Cited by
- 28
- References
- 106
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 9 · 2023: 2
- FWCI
- 0.58
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Seminars in Cancer Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- National Institutes of Health (GR125886, R01CA276301, R01DC020715, R21CA273665, R21EB029493)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- explainable AI, whole slide images, H&E staining, tumor detection, metastasis detection, tumor grade, molecular markers, gene expression, molecular subtyping, genetic markers, mutational burden, microsatellite instability, chromosomal instability, survival prediction, explainability, oncology
- MeSH terms
- Artificial Intelligence, Eosine Yellowish-(YS), Humans, Medical Oncology, Reproducibility of Results, Chromosomal Instability
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