mlsanityData to 7 Oct 2026

Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation

arXiv 2104.13786 ↗First posted 28 Apr 2021eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and obtain labeled data. Obtaining labeled data is especially difficult in biomedical applications, where only trained domain experts can provide labels, which often come in large diversity and complexity. Recently presented approaches for unsupervised detection of visual anomalies approaches omit the need for labeled data and demonstrate promising results in domains, where anomalous samples significantly deviate from the normal appearance. Despite promising results, the performance of such approaches still lags behind supervised approaches and does not provide a one-fits-all solution. In this work, we present an image-to-image translation-based framework that significantly surpasses the performance of existing unsupervised methods and approaches the performance of supervised methods in a challenging domain of cancerous region detection in histology imagery.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 28 Apr 2021.

Builds on

18 of 32 references.

References · 18

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  2. 2016Densely Connected Convolutional Networkscited by 6,691
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  12. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
6 more
  1. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  2. 2017CycleGAN, a Master of Steganographycited by 63
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  4. 2020Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRIcited by 22
  5. 2020Few-Shot Anomaly Detection for Polyp Frames from Colonoscopycited by 19
  6. 2020Manifolds for Unsupervised Visual Anomaly Detectioncited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 2.

4 papers.

  1. 2024AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnosticscited by 8
  2. 2024Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentationcited by 6
  3. 2021Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labelscited by 0
  4. 2023Additive Class Distinction Maps using Branched-GANscited by 0