Large-scale datasets with high-quality labels are desired for training
accurate deep learning models. However, due to the annotation cost, datasets in
medical imaging are often either partially-labeled or small. For example,
DeepLesion is such a large-scale CT image dataset with lesions of various
types, but it also has many unlabeled lesions (missing annotations). When
training a lesion detector on a partially-labeled dataset, the missing
annotations will generate incorrect negative signals and degrade the
performance. Besides DeepLesion, there are several small single-type datasets,
such as LUNA for lung nodules and LiTS for liver tumors. These datasets have
heterogeneous label scopes, i.e., different lesion types are labeled in
different datasets with other types ignored. In this work, we aim to develop a
universal lesion detection algorithm to detect a variety of lesions. The
problem of heterogeneous and partial labels is tackled. First, we build a
simple yet effective lesion detection framework named Lesion ENSemble (LENS).
LENS can efficiently learn from multiple heterogeneous lesion datasets in a
multi-task fashion and leverage their synergy by proposal fusion. Next, we
propose strategies to mine missing annotations from partially-labeled datasets
by exploiting clinical prior knowledge and cross-dataset knowledge transfer.
Finally, we train our framework on four public lesion datasets and evaluate it
on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative
improvement of 49% compared to the current state-of-the-art approach in the
metric of average sensitivity. We have publicly released our manual 3D
annotations of DeepLesion in
this https URL

该研究旨在开发一种通用的病变检测算法，通过利用粗略标注和临床知识，在多个异构数据集上进行训练，实现在多种病变类型下的准确检测。