mlsanityData to 7 Oct 2026

Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification

arXiv 2204.01728 ↗First posted 4 Apr 2022eess.IV cs.CV cs.LGcited by 2

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Abstract (arXiv)

In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learning (GZSL) method that uses self supervised learning (SSL) for: 1) selecting anchor vectors of different disease classes; and 2) training a feature generator. Our approach does not require class attribute vectors which are available for natural images but not for medical images. SSL ensures that the anchor vectors are representative of each class. SSL is also used to generate synthetic features of unseen classes. Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images. Our method is adaptable enough to accommodate class attribute vectors when they are available for natural images.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 62 of its 184 references that are in the map. First posted on arXiv 4 Apr 2022.

Builds on

62 of 184 references.

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Citing papers per year: 2022: 2.

2 papers.

  1. 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1
  2. 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysiscited by 0