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。

通过探索性训练解决通用病变检测任务中的不完整标注问题

Tackling the Incomplete Annotation Issue in Universal Lesion Detection  Task By Exploratory Training

Universal Lesion Detection (ULD) in computed tomography plays an essential
role in computer-aided diagnosis. Promising ULD results have been reported by
multi-slice-input detection approaches which model 3D context from multiple
adjacent CT slices, but such methods still experience difficulty in obtaining a
global representation among different slices and within each individual slice
since they only use convolution-based fusion operations. In this paper, we
propose a novel Slice Attention Transformer (SATr) block which can be easily
plugged into convolution-based ULD backbones to form hybrid network structures.
Such newly formed hybrid backbones can better model long-distance feature
dependency via the cascaded self-attention modules in the Transformer block
while still holding a strong power of modeling local features with the
convolutional operations in the original backbone. Experiments with five
state-of-the-art methods show that the proposed SATr block can provide an
almost free boost to lesion detection accuracy without extra hyperparameters or
special network designs.

该论文提出一种新的 Slice Attention Transformer（SATr）块，将其整合到卷积的 ULD 骨干中，形成混合网络结构，以更好地建模长距离特征依赖关系，同时仍保持使用原始骨干中的卷积操作建模局部特征的强大能力，实验表明该 SATr 块可以在不增加超参数或特殊网络设计的情况下提供准确的病变检测。

SATr: 利用 Transformer 进行切片关注的通用断层扫描病变检测

SATr: Slice Attention with Transformer for Universal Lesion Detection

Lesion detection is an important problem within medical imaging analysis.
Most previous work focuses on detecting and segmenting a specialized category
of lesions (e.g., lung nodules). However, in clinical practice, radiologists
are responsible for finding all possible types of anomalies. The task of
universal lesion detection (ULD) was proposed to address this challenge by
detecting a large variety of lesions from the whole body. There are multiple
heterogeneously labeled datasets with varying label completeness: DeepLesion,
the largest dataset of 32,735 annotated lesions of various types, but with even
more missing annotation instances; and several fully-labeled single-type lesion
datasets, such as LUNA for lung nodules and LiTS for liver tumors. In this
work, we propose a novel framework to leverage all these datasets together to
improve the performance of ULD. First, we learn a multi-head multi-task lesion
detector using all datasets and generate lesion proposals on DeepLesion.
Second, missing annotations in DeepLesion are retrieved by a new method of
embedding matching that exploits clinical prior knowledge. Last, we discover
suspicious but unannotated lesions using knowledge transfer from single-type
lesion detectors. In this way, reliable positive and negative regions are
obtained from partially-labeled and unlabeled images, which are effectively
utilized to train ULD. To assess the clinically realistic protocol of 3D
volumetric ULD, we fully annotated 1071 CT sub-volumes in DeepLesion. Our
method outperforms the current state-of-the-art approach by 29% in the metric
of average sensitivity.

提出了一种新的多任务病变检测方法，应用于多种用于医学成像分析的单病例数据集中，利用临床先验知识和多头多任务病变检测器来检测深度病损数据集中的遗漏注释，从而提高三维体积通用病变检测的性能，与当前最先进的方法相比，平均灵敏度提高了 29%。