ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2019
Robust Histopathology Image Analysis: to Label or to Synthesize?
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
- Embedding-driven dual-branch approach for accurate breast tumor cellularity classification.Scientific reports · 2025Article
- Self-supervised learning for data augmentation in histopathology image segmentation.Scientific reports · 2025Article
- Vision-language foundation models for medical imaging: a review of current practices and innovations.Biomedical engineering letters · 2025Review
- Article
- ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.Biology methods & protocols · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- HcGAN: Harmonic conditional generative adversarial network for efficiently generating high-quality IHC images from H&E.Heliyon · 2024Article
- Spatial Diffusion for Cell Layout Generation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- AnyStar: Domain randomized universal star-convex 3D instance segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
- Topology-Guided Multi-Class Cell Context Generation for Digital Pathology.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer.Scientific data · 2023Article
- SIAN: STYLE-GUIDED INSTANCE-ADAPTIVE NORMALIZATION FOR MULTI-ORGAN HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- ENHANCED SHARP-GAN FOR HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Data-driven color augmentation for H&E stained images in computational pathology.Journal of pathology informatics · 2023Article
- NuSegDA: Domain adaptation for nuclei segmentation.Frontiers in big data · 2023Article
- Deepfake Histologic Images for Enhancing Digital Pathology.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- Recent advances of pathomics in colorectal cancer diagnosis and prognosis.Frontiers in oncology · 2023Review
- Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques.Sensors (Basel, Switzerland) · 2022Review
- Deep learning for survival analysis in breast cancer with whole slide image data.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches, of every tissue type. Although our synthetic patches are not always of high quality, we harness the motley crew of generated samples through a generally applicable importance sampling method. This proposed approach, for the first time, re-weighs the training loss over synthetic data so that the ideal (unbiased) generalization loss over the true data distribution is minimized. This enables us to use a random polygon generator to synthesize approximate cellular structures (i.e., nuclear masks) for which no real examples are given in many tissue types, and hence, GAN-based methods are not suited. In addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and GAN models, to tackle heterogeneity in tissue textures. Compared with existing state-of-the-art supervised models, our approach generalizes significantly better on cancer types without training data. Even in cancer types with training data, our approach achieves the same performance without supervision cost. We release code and segmentation results on over 5000 Whole Slide Images (WSI) in The Cancer Genome Atlas (TCGA) repository, a dataset that would be orders of magnitude larger than what is available today.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.