Research graph
References from AI-based tumor cellularity assessment in digital pathology: A review of methods, datasets, and clinical translation. Local targets link to admitted publications; unresolved targets remain external evidence.
Deep learning techniques for quantification of tumour necrosis in post-neoadjuvant chemotherapy osteosarcoma resection specimens for effective treatment planning
10.18267/j.aip.207 · 2023 · External reference
Application of artificial intelligence in the diagnosis and prognostic prediction of ovarian cancer
10.1016/j.compbiomed.2022.105608 · 2022 · External reference
The role of artificial intelligence in personalized medicine and predictive diagnostics - a narrative review
10.71000/k6cga886 · 2025 · External reference
Artificial intelligence in cancer care: revolutionizing diagnosis, treatment, and precision medicine amid emerging challenges and future opportunities
10.1007/s13205-025-04518-9 · 2025 · External reference
Automated and manual quantification of tumour cellularity in digital slides for tumour burden assessment
10.1038/s41598-019-50568-4 · 2019 · External reference
SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment
2021 · External reference
The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository
10.1007/s10278-013-9622-7 · 2013 · External reference
PAIP 2023: Pathology AI Platform Challenge for tumor segmentation and cellularity estimation [Internet]
2023 · External reference
PAIP 2019: liver cancer segmentation challenge
10.1016/j.media.2020.101854 · 2021 · External reference
PAIP 2020: microsatellite instability prediction in colorectal cancer
10.1016/j.media.2023.102886 · 2023 · External reference
A multi-organ nucleus segmentation challenge
10.1109/tmi.2019.2947628 · 2020 · External reference
Hover-Net: simultaneous segmentation and classification of nuclei in multi-tissue histology images
10.1016/j.media.2019.101563 · 2019 · External reference
PanNuke dataset extension, insights and baselines
2020 · External reference
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2024 · External reference
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2024 · External reference
The Clinical Proteomic Tumor Analysis Consortium (CPTAC): integrating proteomics and genomics in cancer research
2023 · External reference
Assessment of residual breast cancer cellularity after neoadjuvant chemotherapy using digital pathology [data set]
2019 · External reference
A study about color normalization methods for histopathology images
10.1016/j.micron.2018.07.005 · 2018 · External reference
Minimal high-resolution patches are sufficient for whole slide image representation via cascaded dual-scale reconstruction
2025 · External reference
A high-resolution tile-based approach for classifying biological regions in whole-slide histopathological images
10.1007/978-3-319-03005-0_71 · 2014 · External reference
Fully automatic content-aware tiling pipeline for pathology whole slide images
10.1016/j.ibmed.2025.100318 · 2025 · External reference
Direct cellularity estimation on breast cancer histopathology images using transfer learning
10.1155/2019/3041250 · 2019 · External reference
Breast tumor cellularity assessment using deep neural networks
2019 · External reference
Tumor cellularity assessment using artificial intelligence trained on immunohistochemistry-restained slides improves selection of lung adenocarcinoma samples for molecular testing
10.1016/j.ajpath.2025.01.009 · 2025 · External reference
Tumor cellularity assessment of breast histopathological slides via instance segmentation and pathomic features explainability
2023 · External reference
Detecting and segmenting cell nuclei in two-dimensional microscopy images
10.4103/2153-3539.192810 · 2016 · External reference
Automatic tumor cellularity measurement: AI-based pipeline for multi-organ pathology imaging
2024 · External reference
Residual tumor cellularity assessment of breast cancer after neoadjuvant therapy using image transformer
2024 · External reference
MAMILNet: advancing precision oncology with multi-scale attentional multi-instance learning for whole slide image analysis
2024 · External reference
Joint modeling histology and molecular markers for cancer classification
10.1016/j.media.2025.103505 · 2025 · External reference
A multi-scale conditional deep model for tumor cell ratio counting
2021 · External reference
Multi-class cancer diagnosis on histopathological images with deep ensemble learning model
10.1016/j.compbiomed.2025.111381 · 2026 · External reference
A multi-technique ensemble model leveraging attention mechanism and image processing for enhanced colorectal tumor detection
10.1038/s41598-025-25986-2 · 2025 · External reference
Boosting predictive accuracy in tumor cellularity evaluation with AI-powered ensemble methods
2025 · External reference
Towards a general-purpose foundation model for computational pathology
10.1038/s41591-024-02857-3 · 2024 · External reference
A visual-language foundation model for computational pathology
10.1038/s41591-024-02856-4 · 2024 · External reference
A whole-slide foundation model for digital pathology from real-world data
10.1038/s41586-024-07441-w · 2024 · External reference
A self-supervised contrastive learning approach for whole slide image representation in digital pathology
10.1016/j.jpi.2022.100133 · 2022 · External reference
A clinical benchmark of public self-supervised pathology foundation models
10.1038/s41467-025-58796-1 · 2025 · External reference
Domain-adaptive semi-supervised learning for efficient rare pathological lesion detection with minimal annotation
10.1038/s41746-025-02160-6 · 2025 · External reference
Effective and efficient active learning for deep learning-based tissue image analysis
10.1093/bioinformatics/btad138 · 2023 · External reference
Breast tumor cellularity assessment using deep neural networks
2019 · External reference
NuCLS: a scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer
10.1093/gigascience/giac037 · 2022 · External reference
Integrating AI-powered digital pathology and imaging mass cytometry identifies key classifiers of tumor cells, stroma, and immune cells in non-small cell lung cancer
10.1158/0008-5472.can-23-1698 · 2024 · External reference
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Digital pathology: advantages, limitations and emerging perspectives
10.3390/jcm9113697 · 2020 · External reference
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2024 · External reference
Artificial intelligence-based tool for tumor detection and quantitative tissue analysis in colorectal specimens
2023 · External reference
Evaluating cellularity estimation methods: comparing AI counting with pathologists' visual estimates
10.3390/diagnostics14111115 · 2024 · External reference
Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline
10.1007/s00428-024-03794-9 · 2025 · External reference
