mlsanityData to 14 Oct 2026

Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types

arXiv 2510.11182 ↗First posted 13 Oct 2025eess.IV cs.AI cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Deep learning is expected to aid pathologists by automating tasks such as tumour segmentation. We aimed to develop one universal tumour segmentation model for histopathological images and examine its performance in different cancer types. The model was developed using over 20 000 whole-slide images from over 4 000 patients with colorectal, endometrial, lung, or prostate carcinoma. Performance was validated in pre-planned analyses on external cohorts with over 3 000 patients across six cancer types. Exploratory analyses included over 1 500 additional patients from The Cancer Genome Atlas. Average Dice coefficient was over 80% in all validation cohorts with en bloc resection specimens and in The Cancer Genome Atlas cohorts. No loss of performance was observed when comparing the universal model with models specialised on single cancer types. In conclusion, extensive and rigorous evaluations demonstrate that generic tumour segmentation by a single model is possible across cancer types, patient populations, sample preparations, and slide scanners.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 68 references that are in the map. First posted on arXiv 13 Oct 2025.

Builds on

25 of 68 references.

References · 25

  1. 2023Segment Anythingcited by 8,234
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  6. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  7. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  8. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  9. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
  10. 2018Group Normalizationcited by 1,524
  11. 2022Vision Transformer Adapter for Dense Predictionscited by 565
  12. 2018Albumentations: fast and flexible image augmentationscited by 560
13 more
  1. 2021High-Performance Large-Scale Image Recognition Without Normalizationcited by 360
  2. 2019Fixing the train-test resolution discrepancycited by 209
  3. 2022Metrics reloaded: Recommendations for image analysis validationcited by 195
  4. 2024Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathologycited by 161
  5. 2018Why rankings of biomedical image analysis competitions should be interpreted with carecited by 91
  6. 2021Characterizing signal propagation to close the performance gap in unnormalized ResNetscited by 88
  7. 2024Phikon-v2, A large and public feature extractor for biomarker predictioncited by 74
  8. 2020A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysiscited by 45
  9. 2020HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide imagescited by 45
  10. 2019Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecturecited by 20
  11. 2025Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignosticscited by 11
  12. 2022Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutationcited by 7
  13. 2019Learning to segment images with classification labelscited by 5

Built on by

Not cited yet.