Hard Sample Aware Noise Robust Learning for Histopathology Image Classification

12/05/2021
by   Chuang Zhu, et al.
0

Deep learning-based histopathology image classification is a key technique to help physicians in improving the accuracy and promptness of cancer diagnosis. However, the noisy labels are often inevitable in the complex manual annotation process, and thus mislead the training of the classification model. In this work, we introduce a novel hard sample aware noise robust learning method for histopathology image classification. To distinguish the informative hard samples from the harmful noisy ones, we build an easy/hard/noisy (EHN) detection model by using the sample training history. Then we integrate the EHN into a self-training architecture to lower the noise rate through gradually label correction. With the obtained almost clean dataset, we further propose a noise suppressing and hard enhancing (NSHE) scheme to train the noise robust model. Compared with the previous works, our method can save more clean samples and can be directly applied to the real-world noisy dataset scenario without using a clean subset. Experimental results demonstrate that the proposed scheme outperforms the current state-of-the-art methods in both the synthetic and real-world noisy datasets. The source code and data are available at https://github.com/bupt-ai-cz/HSA-NRL/.

READ FULL TEXT

page 1

page 6

page 11

research
05/28/2023

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning

Label-noise learning (LNL) aims to increase the model's generalization g...
research
09/12/2018

Hyperspectral Image Classification in the Presence of Noisy Labels

Label information plays an important role in supervised hyperspectral im...
research
07/02/2022

Less is More: Adaptive Curriculum Learning for Thyroid Nodule Diagnosis

Thyroid nodule classification aims at determining whether the nodule is ...
research
10/29/2020

Suppressing Mislabeled Data via Grouping and Self-Attention

Deep networks achieve excellent results on large-scale clean data but de...
research
07/15/2021

Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining

Many real-world applications involve the use of Optical Character Recogn...
research
02/18/2021

Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction

Early detection of suicidal ideation in depressed individuals can allow ...
research
07/29/2021

Learning with Noisy Labels for Robust Point Cloud Segmentation

Point cloud segmentation is a fundamental task in 3D. Despite recent pro...

Please sign up or login with your details

Forgot password? Click here to reset