Universal lesion detection has great value for clinical practice as it aims
to detect various types of lesions in multiple organs on medical images. Deep
learning methods have shown promising results, but demanding large volumes of
annotated data for training. However, annotating medical images is costly and
requires specialized knowledge. The diverse forms and contrasts of objects in
medical images make fully annotation even more challenging, resulting in
incomplete annotations. Directly training ULD detectors on such datasets can
yield suboptimal results. Pseudo-label-based methods examine the training data
and mine unlabelled objects for retraining, which have shown to be effective to
tackle this issue. Presently, top-performing methods rely on a dynamic
label-mining mechanism, operating at the mini-batch level. However, the model's
performance varies at different iterations, leading to inconsistencies in the
quality of the mined labels and limits their performance enhancement. Inspired
by the observation that deep models learn concepts with increasing complexity,
we introduce an innovative exploratory training to assess the reliability of
mined lesions over time. Specifically, we introduce a teacher-student detection
model as basis, where the teacher's predictions are combined with incomplete
annotations to train the student. Additionally, we design a prediction bank to
record high-confidence predictions. Each sample is trained several times,
allowing us to get a sequence of records for each sample. If a prediction
consistently appears in the record sequence, it is likely to be a true object,
otherwise it may just a noise. This serves as a crucial criterion for selecting
reliable mined lesions for retraining. Our experimental results substantiate
that the proposed framework surpasses state-of-the-art methods on two medical
image datasets, demonstrating its superior performance.

通过引入创新的探索式训练方法，结合不完整注释和预测记录来选择可靠的矿化病灶进行重新训练，我们提出的框架在两个医学图像数据集上展现了卓越的性能，超越了现有最优方法。