mlsanityData to 7 Oct 2026

Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning

arXiv 2111.07646 ↗First posted 15 Nov 2021cs.CV cs.LG eess.IVcited by 4

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Abstract (arXiv)

Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis. Since such images are obtained after invasive tissue resection quick diagnosis is challenging under the existing paradigm. We propose a method to predict Gleason grades from magnetic resonance (MR) images which are non-interventional and easily acquired. We solve the problem in a generalized zero-shot learning (GZSL) setting since we may not access training images of every disease grade. Synthetic MRI feature vectors of unseen grades (classes) are generated by exploiting Gleason grades' ordered nature through a conditional variational autoencoder (CVAE) incorporating self-supervised learning. Corresponding histopathology features are generated using cycle GANs, and combined with MR features to predict Gleason grades of test images. Experimental results show our method outperforms competing feature generating approaches for GZSL, and comes close to performance of fully supervised methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 41 of its 147 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

41 of 147 references.

References · 41

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29 more
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Cited by year

Citing papers per year: 2022: 4.

4 papers.

  1. 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
  2. 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
  3. 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1
  4. 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysiscited by 0