To improve classification accuracy even with these noisy inputs and labels in histopathology, we propose a novel method for robust feature generation using an ...

In this paper, we address the problem of learning robust models for image classification in the face of label noise and weak supervision. We use adversarial ...

Nov 4, 2021 · To improve classification accuracy even with these noisy inputs and labels in histopathology, we propose a novel method for robust feature ...

To improve classification accuracy even with these noisy inputs and labels in histopathology, we propose a novel method for robust feature generation using an ...

Robust Classification of Histology Images Exploiting Adversarial Auto Encoders. N. Kurian, G. Singh, P. Hebbar, S. Kodate, S. Rane, and A. Sethi.

Robust Classification of Histology Images Exploiting Adversarial Auto Encoders · Computer Science, Medicine. 2021 43rd Annual International Conference of the…

To improve classification accuracy even with these noisy inputs and labels in histopathology, we propose a novel method for robust feature generation using an ...

They showed that classification accuracy drops from above. 87% on normal medical images to almost 0% on adversarial examples. Work in (Paschali et al., 2018) ...

This study will focus on the robust detection of pneumonia in chest X-ray images through CNN-based models. Various adversarial attacks and defense strategies ...

Robust Classification of Histology Images Exploiting Adversarial Auto Encoders · Sample Specific Generalized Cross Entropy for Robust Histology Image ...