  Immune repertoire classification, a typical multiple instance learning (MIL)
problem, is a frontier research topic in computational biology that makes
transformative contributions to new vaccines and immune therapies. However, the
traditional instance-space MIL, directly assigning bag-level labels to
instances, suffers from the massive amount of noisy labels and extremely low
witness rate. In this work, we propose a noisy-label-learning formulation to
solve the immune repertoire classification task. To remedy the inaccurate
supervision of repertoire-level labels for a sequence-level classifier, we
design a robust training strategy: The initial labels are smoothed to be
asymmetric and are progressively corrected using the model's predictions
throughout the training process. Furthermore, two models with the same
architecture but different parameter initialization are co-trained
simultaneously to remedy the known "confirmation bias" problem in the
self-training-like schema. As a result, we obtain accurate sequence-level
classification and, subsequently, repertoire-level classification. Experiments
on the Cytomegalovirus (CMV) and Cancer datasets demonstrate our method's
effectiveness and superior performance on sequence-level and repertoire-level
tasks.
