Our publications
Peer-reviewed from the start.
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- 06Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.
- 07Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology.
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- 09Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning.
- 10Virtual multiplex immunofluorescence identifies lymphocyte subsets predictive of response to neoadjuvant therapy.
- 11Integration of gene expression and digital histology to predict treatment-specific responses in breast cancer.
- 12Artificial intelligence differentiates prefibrotic primary myelofibrosis with thrombocytosis from essential thrombocythemia using digitized bone marrow biopsy images.
- 13Development and validation of a multimodal clinical, pathologic, and genomic model for breast cancer recurrence (AI-Path).
Built on Slideflow
Used in research worldwide.
Slideflow is cited or extended by more than 100 studies. A selection of independent work is listed below, grouped by focus. The complete, always-current list lives on Google Scholar.
Methods & tools 28
- LazySlide: accessible and interoperable whole-slide image analysis.
- The impact of tissue detection on diagnostic AI algorithms in (prostate) digital pathology.
- Comparative analysis of pathology foundation models for automated detection of tertiary lymphoid structures in H&E digital pathology.
- DIANNE: segmentation-free localization of histology differential attributes.
- CellDX AI Autopilot: agent-guided training and deployment of pathology classifiers.
- From binary to continuous: learning to continuously quantify histopathological patterns from binary labeled images.
- Deep learning-integrated digital pathology system for early-stage cancer screening using high-resolution tissue images.
- Decoding cancer tissues: a comparative deep learning view of breast histopathology.
- Preprocessing in colorectal cancer histopathology: a prerequisite for effective computational analysis.
- Interpretable AI driven histopathological image analysis.
- Integrative whole slide image and spatial transcriptomics analysis with QuST and QuPath.
- Self-supervised learning for data augmentation in histopathology image segmentation.
- PathBench-MIL: a comprehensive AutoML and benchmarking framework for multiple instance learning in histopathology.
- ORCA: a comprehensive AI-driven platform for digital pathology analysis and biomarker discovery.
- Reusable specimen-level inference in computational pathology.
- WSInsight as a cloud-native pipeline for single-cell pathology inference on whole-slide images.
- Toward quantum-enabled biomarker discovery: an outlook from q4bio.
- Histolytics: a panoptic spatial analysis framework for interpretable histopathology.
- The 3D visualization of digitalized pathological serial sections on a native resolution.
- Dynamic graph representation for WSI classification: a knowledge-aware attention mechanism.
- A dataset for artefact detection of whole slide images in digital pathology.
- Parallel computing for efficient histopathological image classification: GPU-accelerated deep learning for breast cancer detection.
- Converting whole slide images from DICOM to ScanScope Virtual Slide-like TIFF: a practical workaround.
- Advances in risk prediction, explainability, and accessible AI in computational pathology.
- Multimodal co-attention fusion network with online data augmentation for cancer subtype classification.
- Open and reusable deep learning for pathology with WSInfer and QuPath.
- Generalizing AI-driven assessment of immunohistochemistry across immunostains and cancer types: a universal IHC analyzer.
- Développement d'un plugin pour le visualisateur d'images histopathologiques Sectra (decision support based on DL).
Clinical applications 25
- Weakly supervised deep learning for cutaneous squamous and basal cell carcinoma in whole-slide histopathology.
- AI-based histopathology analysis predicts checkpoint inhibitor response in advanced melanoma and identifies patterns associated with response.
- Domain generalisation challenges in breast cancer molecular classification using foundation models.
- Leveraging interpretable AI for deciphering signature histopathologic patterns (LCV vs MVO).
- Pathogenomic analysis reveals clinically relevant epithelial-mesenchymal plasticity in esophageal squamous cell carcinoma.
- Integrating quantitative histology with clinical data improves prediction of cervical intraepithelial neoplasia regression.
- Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype.
- Learning the forest before the trees: artificial intelligence and thymic tumour pathology.
- Multiple instance learning using pathology foundation models effectively predicts kidney disease diagnosis and clinical classification.
- An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations.
- Deep learning-based classification of early-stage mycosis fungoides and benign inflammatory dermatoses on H&E WSIs.
- Translating features to findings: deep learning for melanoma subtype prediction.
- Deep learning discriminates thymic epithelial tumors histological subtypes using digital pathology.
- Deep learning-based classification of colorectal cancer in histopathology images.
- Weakly supervised deep learning-based detection of serous tubal intraepithelial carcinoma in fallopian tubes.
- Cross-domain approach for automated thyroid classification using Diff-Quick images.
- Multiple-instance learning for thyroid gland disease classification: a hands-on experience.
- Implementing trust in NSCLC diagnosis with a conformalized uncertainty-aware AI framework in whole-slide images.
- Predictive modelling of colorectal cancer using tumor infiltrating lymphocytes: a deep learning approach.
- Automated identification of histological lesions in nonmodel organisms: reinvigorating environmental science.
- Demographic bias in misdiagnosis by computational pathology models.
- Deep learning histology for prediction of lymph node metastases and tumor regression after neoadjuvant FLOT therapy.
- AI-powered classification of ovarian cancers based on histopathological images.
- Predicting the HER2 status in oesophageal cancer from tissue microarrays using convolutional neural networks.
- Performance comparison between multi-center histopathology datasets of a weakly-supervised DL model for pancreatic ductal adenocarcinoma detection.
Reviews & surveys 19
- Artificial intelligence in medical diagnostics: foundations, clinical applications, and future directions.
- The role of whole slide imaging in AI-based digital pathology: current challenges and future directions.
- A comprehensive review of multimodal large language models for medical imaging and omics data.
- Exploring transparency in pathological image analysis: a comprehensive review of explainable AI (XAI) techniques.
- Smart lies and sharp eyes: pragmatic artificial intelligence for cancer pathology — promise, pitfalls, and access pathways.
- Cracking the code: computational image analysis tools for histopathological and morphometric insights.
- Artificial intelligence and machine learning in diagnostic pathology: a systematic review.
- AI-driven discovery in protein science for immunology and infectious disease research.
- Current AI technologies in cancer diagnostics and treatment.
- Artificial intelligence-based biomarkers for treatment decisions in oncology.
- Advancing open-source visual analytics in digital pathology: a systematic review of tools, trends, and clinical applications.
- The application of artificial intelligence in periprosthetic joint infection.
- Artificial intelligence in medicine: a specialty-level overview of emerging AI trends.
- Harnessing artificial intelligence in head and neck oncology practice: data, diagnosis, and therapy.
- Computer vision methods under rapid evolution for pathology image tasks.
- Survey on whole slide image in pathology: deep learning and machine learning approaches.
- Artificial intelligence applications in oral cancer and oral dysplasia.
- The quest for the application of artificial intelligence to whole slide imaging.
- Perspective chapter: computer vision-based digital pathology for central nervous system tumors — state-of-the-art and current advances.