Experts’ cognition-driven safe noisy labels learning for precise segmentation of residual tumor in breast cancer. (arXiv:2304.07295v1 [q-bio.QM])

Precise segmentation of residual tumor in breast cancer (PSRTBC) after
neoadjuvant chemotherapy is a fundamental key technique in the treatment
process of breast cancer. However, achieving PSRTBC is still a challenge, since
the breast cancer tissue and tumor cells commonly have complex and varied
morphological changes after neoadjuvant chemotherapy, which inevitably
increases the difficulty to produce a predictive model that has good
generalization with machine learning. To alleviate this situation, in this
paper, we propose an experts’ cognition-driven safe noisy labels learning
(ECDSNLL) approach. In the concept of safe noisy labels learning, which is a
typical type of safe weakly supervised learning, ECDSNLL is constructed by
integrating the pathology experts’ cognition about identifying residual tumor
in breast cancer and the artificial intelligence experts’ cognition about data
modeling with provided data basis. We show the advantages of the proposed
ECDSNLL approach and its promising potentials in addressing PSRTBC. We also
release a better predictive model for achieving PSRTBC, which can be leveraged
to promote the development of related application software.