Deep learning has the potential to revolutionize medical practice by
automating and performing important tasks like detecting and delineating the
size and locations of cancers in medical images. However, most deep learning
models rely on augmentation techniques that treat medical images as natural
images. For contrast-enhanced Computed Tomography (CT) images in particular,
the signals producing the voxel intensities have physical meaning, which is
lost during preprocessing and augmentation when treating such images as natural
images. To address this, we propose a novel preprocessing and intensity
augmentation scheme inspired by how radiologists leverage multiple viewing
windows when evaluating CT images. Our proposed method, window shifting,
randomly places the viewing windows around the region of interest during
training. This approach improves liver lesion segmentation performance and
robustness on images with poorly timed contrast agent. Our method outperforms
classical intensity augmentations as well as the intensity augmentation
pipeline of the popular nn-UNet on multiple datasets.

通过窗口位移技术改进医学图像的深度学习模型，提高肝脏病变分割性能并增强对于对比剂不准确的图像的鲁棒性。

类似放射科医生的视角：面向深度学习的 CT 图像增强的滑动窗口

View it like a radiologist: Shifted windows for deep learning  augmentation of CT images

Transfer learning and joint learning approaches are extensively used to
improve the performance of Convolutional Neural Networks (CNNs). In medical
imaging applications in which the target dataset is typically very small,
transfer learning improves feature learning while joint learning has shown
effectiveness in improving the network's generalization and robustness. In this
work, we study the combination of these two approaches for the problem of liver
lesion segmentation and classification. For this purpose, 332 abdominal CT
slices containing lesion segmentation and classification of three lesion types
are evaluated. For feature learning, the dataset of MICCAI 2017 Liver Tumor
Segmentation (LiTS) Challenge is used. Joint learning shows improvement in both
segmentation and classification results. We show that a simple joint framework
outperforms the commonly used multi-task architecture (Y-Net), achieving an
improvement of 10% in classification accuracy, compared to a 3% improvement
with Y-Net.

本论文研究将联合学习和迁移学习两种方法结合运用于肝脏病变分割和分类问题上，在医学影像应用中，由于目标数据集通常非常小，迁移学习可以提高特征学习的效果，而联合学习对于提高网络的泛化和鲁棒性更为有效。

通过迁移学习实现肝脏病变分割和分类

Joint Liver Lesion Segmentation and Classification via Transfer Learning

Liver lesion segmentation is an important step for liver cancer diagnosis,
treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation
Challenge) provides a common testbed for comparing different automatic liver
lesion segmentation methods. We participate in this challenge by developing a
deep convolutional neural network (DCNN) method. The particular DCNN model
works in 2.5D in that it takes a stack of adjacent slices as input and produces
the segmentation map corresponding to the center slice. The model has 32 layers
in total and makes use of both long range concatenation connections of U-Net
[1] and short-range residual connections from ResNet [2]. The model was trained
using the 130 LiTS training datasets and achieved an average Dice score of 0.67
when evaluated on the 70 test CT scans, which ranked first for the LiTS
challenge at the time of the ISBI 2017 conference.

利用深度卷积神经网络，以 2.5D 方式对肝脏 CT 图像进行分割，并在 130 个 LiTS 训练数据集上进行训练，在 70 个测试 CT 扫描上取得了平均 Dice 分数 0.67，时隔 ISBI2017 会议后获得了该领域的第一名。