[CV] Medical Image Analysis

Introduction

医疗AI是现如今工业界和学术界都非常火热的方向,也是AI落地非常有价值的方向。因毕业论文需要涉及Medical Image Analysis相关的方向,所以本文旨在收集并梳理Deep Learning在Medical Image Analysis领域一些具有代表性的Paper以及Report。

@LucasXU注:本文长期更新。

CNN for Medical Image Analysis. Full Training or Fine Tuning?

Paper: Convolutional neural networks for medical image analysis: Full training or fine tuning?

Our experiments consistently demonstrated that

  1. the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch;
  2. fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch;
  3. neither shallow tuning nor deep tuning was the optimal choice for a particular application; and
  4. our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

简而言之,尽管我们观念上可能认为ImageNet中的natural images data distribution(例如semantic meaning, image resolution等等)和医学图像的data distribution差距很大,fine-tune似乎不可行,但是作者做了3类实验(classification/detection/segmentation),发现从ImageNet上pretrain的模型到医学图像分析认为上进行fine-tune是可行的,而且会带来性能提升,收敛也更快

Deep Learning for Skin Cancer Classification

Paper: Dermatologist-level classification of skin cancer with deep neural networks

Inception v3 (pretrained on ImageNet)用于做skin cancer classification,算法效果超过了人类医生。模型没什么太大的新意,记一下医学图像领域常用的两个metric:
$$
sensitivity=\frac{True Positive}{Positive}
$$

$$
specificity=\frac{True Negative}{Negative}
$$

Deep Learning for Chest X-rays Recognition

Paper: CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

Deep Model尽管性能超群,但是Interpretability却非常差,尤其是AI在医学、金融等领域的应用,就会非常看重Interpretability(所以好多金融系统依然还在用规则,而非Machine Learning)。CheXNet是Andrew Ng团队的成果,基础idea是DenseNet做classification,CAM做可视化,来显示哪些区域是最discriminative的。

Clinical Skin Lesion Diagnosis using Representations Inspired by Dermatologist Criteria

Paper: Clinical skin lesion diagnosis using representations inspired by dermatologist criteria

这是一篇发表在CVPR’18上的文章,主要contribution是设计了一种novel的手工feature来进行skin disease recognition。该手工特征主要涉及Structure, Color, Shape。并且在SD-198 dataset上取得了State-of-the-art的效果。医学图像和我们常规的natural image不同,intra-class diversity非常大,我看了SD-198的图片,DNN实在是非常不好分。

  • 对于Structure,由texture distribution来表示;主要通过multiple space的symmetry property来决定。
  • 对于Color,通过ColorName来和skin lesion相关的color来表示;以及通过引入每种颜色的continuous value来区分颜色相同、但颜色程度不同的情况。
  • 对于Shape,作者利用peripheral symmetry和constrained compactness来进行表示。

Criteria of Skin Disease

作者通过调研了大量skin lesion的文献,总结了skin diagnosis的ABCD criteria:

  • A (Asymmetry): lesion shape, contour, colors and structures.
  • B (Border): ill-defined and irregular border of lesion.
  • C (Color variegation): skin lesion的color并非统一。
  • D (Diameter): skin lesion的大致直径。

Medical Representations

介绍完了上面的ABCD criteria,下面我们直接来看本文的核心——medical representation。Medical representation可以通过如下方式生成:

  • skin lesion的面积、直径(D)、边缘(B)由连通区域的像素点数量,以及主轴决定。
  • A可由连通区域的geometric information决定。
  • C可通过不同color space共同决定,这样可以保证即使有略微的颜色变化(例如illumination影响),依然具备discrimination。

下面我们来进行详细的讲解。

Structure Representation

Multi-Space Texture of Lesion (MST-L)

为了有效地表示skin lesion的structure,我们基于不同的color space来计算texture representation,来减少环境的影响。对于每一张clinical image $x$,我们提出了multi-space texture ($MST(x)$):
$$
MST(x)=[G_i(x)]_{i=1}^K
$$
$G_i(x)$代表从第$i$个color channel抽取的texture feature,$K$代表color space总数。在本文中,作者使用了3个color space,即Hue, Saturation, Brightness;并对每一个color space利用SIFT进行feature extraction。

Texture Symmetry of Lesion (TS-L)

skin lesion的不对称性也是个非常discriminative的特征。作者利用MBD+算法进行lesion region检测,然后根据主轴对检测出的区域划分为两个部分——$L(x)_1$和$L(x)_2$。然后对每个部分进行texture feature extraction,最后对第$i$个color space的texture symmetry进行如下表示:
$$
TS_i(x)=[G_i(L(x)_1), G_i(L(x)_2), S_i(x)]
$$
其中,$S_i(x)=\{|g_{ij}^1-g_{ij}^2|\}_{j=1}^d$,$d$代表抽取feature的dimension,$g_{ij}^1$和$g_{ij}^2$代表第$j$个entry $G_i(L(x)_1)$和$G_i(L(x)_2)$。我们在Hue space中进行texture symmetry度量,因为Hue space在不同light intensity情况下依然是scale-invariant + shift-invariant

Color Representation

Color Name of Lesion (CN-L)

在$L\times a\times b$ space中,我们对每一个color bin计算pprobability vector $P=[p(C_l|c)]_{l=1}^M$:
$$
[p(C_l|c)]_{l=1}^M\propto \sum_i^N p(C_l|c_i)g^{\sigma}(|c_i-c|_{Lab})
$$
$c$代表color bin的original value,$c_i$代表$c$的Lab value,$N=387$代表color bin的总数,$C$代表basic colors set。$g^{\sigma}$代表$\sigma=5$的guassian kernel。作者通过对skin lesion的调研,设置$M=8$,$C=\{red, pink, purple, yellow, white, black, brown, blue\}$。最终lesion的color name $CN(x)$为:
$$
CN(x)=\mathop{argmax} \limits_{C_l}[p(C_l|c)]_{l=1}^M
$$

Continuous Color Values of Lesion (CCV-L)

除了CN-L,作者还对每个lesion设置了continuous value来代表color的不同程度。对于每个bin $c$,我们定义continuous color value $CCV(c)$:
$$
CCV(c)\propto p(C,c)\times \theta(c)
$$

其中$p(C,c)$代表将color bin $c$ 映射到其最近color name $C$的概率。$\theta(c)$代表pixel的权重值:
$$
\theta(c)=\sum_{|c|}n(c)u(c)
$$
$n(c)$代表图片中对于color的frequency,$u(c)$代表RGB space中color bin $c$的color value。

Shape Representation

对于形状特征,本文主要关注 (1) shape symmetry,(2) lesion的constrained compactness。

Peripheral Symmetry of Lesion (PS-L)

同样应用MBD+算法进行lesion region detection,划分等面积的两个部分$L(x)_1$和$L(x)_2$。然后计算两个parts lesion的peripheral symmetry:
$$
PS(x)=F(A(L(x)^1), A(L(x)^2))
$$
其中$A(\cdot)$代表从lesion中抽取的feature,$F(\cdot, \cdot)$代表concatenation operation。

Adaptive Compactness of Lesion (AC-L)

对于skin disease region,圆的近似程度对diagnosis非常有帮助。因此可以用如下方式来表示:
$$
Com=\frac{4\pi A}{P^2}
$$
$A$代表面积,$P$代表lesion的周长。

lesion的面积表示:
$$
A_L=\sum_{z\in L(x)}p(C|c,z)
$$
$z$代表lesion $L(x)$的像素,$p(C|c,z)$是在color name feature中将color映射到特定颜色类型的概率,$p(C|c,z)$反映了像素$z$位于lesion center的重要性。

实验结果表明,本文设计的特征比传统方法/DCNN都要好,metric为Accuracy和Sensitivity。

Reference

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  2. Tajbakhsh, Nima, et al. “Convolutional neural networks for medical image analysis: Full training or fine tuning?.” IEEE transactions on medical imaging 35.5 (2016): 1299-1312.
  3. De Fauw, Jeffrey, et al. “Clinically applicable deep learning for diagnosis and referral in retinal disease.” Nature medicine 24.9 (2018): 1342.
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  8. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, et al. (2018) Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLOS Medicine 15(11): e1002686. https://doi.org/10.1371/journal.pmed.1002686
  9. Esteva, Andre, et al. “Dermatologist-level classification of skin cancer with deep neural networks.” Nature 542.7639 (2017): 115.
  10. Codella, Noel CF, et al. “Deep learning ensembles for melanoma recognition in dermoscopy images.” IBM Journal of Research and Development 61.4 (2017): 5-1.
  11. Haofu, Liao, and Jiebo Luo. “A Deep Multi-Task Learning Approach to Skin Lesion Classification.” Workshops at the Thirty-First AAAI Conference on Artificial Intelligence. 2017.
  12. Yang, Jufeng, et al. “Clinical skin lesion diagnosis using representations inspired by dermatologist criteria.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
  13. Ge, Zongyuan, et al. “Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images.” International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2017.

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