  Noisy labels hurt deep learning-based supervised image classification
performance as the models may overfit the noise and learn corrupted feature
extractors. For natural image classification training with noisy labeled data,
model initialization with contrastive self-supervised pretrained weights has
shown to reduce feature corruption and improve classification performance.
However, no works have explored: i) how other self-supervised approaches, such
as pretext task-based pretraining, impact the learning with noisy label, and
ii) any self-supervised pretraining methods alone for medical images in noisy
label settings. Medical images often feature smaller datasets and subtle inter
class variations, requiring human expertise to ensure correct classification.
Thus, it is not clear if the methods improving learning with noisy labels in
natural image datasets such as CIFAR would also help with medical images. In
this work, we explore contrastive and pretext task-based self-supervised
pretraining to initialize the weights of a deep learning classification model
for two medical datasets with self-induced noisy labels -- NCT-CRC-HE-100K
tissue histological images and COVID-QU-Ex chest X-ray images. Our results show
that models initialized with pretrained weights obtained from self-supervised
learning can effectively learn better features and improve robustness against
noisy labels.
