1. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets_2017
方法: We present a methodology that extracts and pools low- to mid-level features using a pretrained CNN and fuses them with handcrafted radiomic features computed using conventional CADx methods.
Features are extracted from the five max-pooling layers, averagepooled across the channel (third) dimension, and normalized with L2 norm.
没啥用,利用VGG19进行特征提取,让后和传统特征融合将每个pooling层的特征输出1*1*dim维度的特征,使用了L2规则
2. A Method of Ultrasonic Image Recognition for Thyroid Papillary Carcinoma Based on Deep Convolution Neural Network_14 March 2018
该团队还有一篇文章基本一样,数据:307个人
the Fast Region-based Convolutional Network method (FasterRCNN) network is `improved and normalized by connecting the fourth layer and the fifth layer` of the shared convolution layer in the Faster RCNN network. Then, a multi-scale ultrasound image is used at the time of input.
将VGG16的第四层和第五层连接,同时将多尺度的超声图像输入.
The experimental results show that compared with the original Faster RCNN network, the proposed method has higher recognition accuracy, shorter training time and higher efficiency in ultrasonic image recognition of thyroid papillary carcinoma.
与传统的FasterRCNN相比这个方法更好,时间短,更精确
本文VGG16连接图
使用原始FasterRCNN在本文map = 0.6,原因就是超声图像各种不如自然图像.
将层间连接之前使用L2 和尺度变化,然后输入多尺度的图像
Each tensor is normalized using L2, and normalization is accomplished within each pixel of the set feature tensor. After normalization, scaling is applied separately on each tensor.
Our experiments have proven that the feature in different size range can be learned through multi-scale image input, which increases the robustness, reduces the influence of down sampling on the feature representation, improves the extraction efficiency of the original feature of the image, and raises the accuracy of cancer feature recognition.
结果图
3. A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography_MICCAI_2016
The third step divides the images based on a grid representation to multiple overlapping sub images (parts) which are then used to train and test a modified Faster-RCNN
为了解决小尺度肿瘤和对比度低问题将层间连接
features from lower levels of the CNN need to be taken into the account when making the decision as they are the only ones looking on the considered region proposals in the high enough resolution
类似三通道的FasterRCNN
网络结构
4. Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests_2015
The first stage classifier consists of a multi-scale deep belief network that selects suspicious regions to be further processed by a two-level cascade of deep convolutional neural networks. The regions that survive this deep learning analysis are then processed by a two-level cascade of random forest classifiers that use morphological and texture features extracted from regions selected along the cascade. Finally, regions that survive the cascade of random forest classifiers are combined using connected component analysis to produce state-of-the-art results.
5. Automatic Colon Polyp Detection Using Region Based Deep CNN and Post Learning Approaches_2018
We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system.
将FasterRCNN的特征提取用inception resnet实现,同时增加了一种尺度的anchor和更改阈值
Two versions of Inception Resnet have been introduced in [35] and we use a deeper version called Inception Resnet-v2.
6. Context-aware pedestrian detection especially for small-sized instances with Deconvolution Integrated Faster RCNN (DIF R-CNN)
解决行人检测中的小目标问题,利用解卷积引入新的环境特征,能够检测像素＜50的人
Furthermore, the state-of-the-art CNN-based model (Inception-ResNet) is exploited to provide a rich and discriminative hierarchy of feature representations
Additionally, atrous convolution is adopted to enlarge the receptive field of the synthetic feature map
atrous convolution :稀疏卷积有洞的卷积
网络结构
atrous convolution
7. Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography_2017
结构
结构也是三通道的FasterRCNN
8. Large scale deep learning for computer aided detection of mammographic lesions_2016
将手动选择的特征与cnn的特征结合,得到cnn学不到的特征
In this paper we provide a head-to-head comparison between a state-of-the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN)
9. Liver Fibrosis Classification Based on Transfer Learning and FCNet for Ultrasound Images_2017
In this paper, we propose a novel liver fibrosis classification method based on transfer learning (TL) using VGGNet and a deep classifier called fully connected network (FCNet).
10. R-FCN: Object Detection via Region-based Fully Convolutional Networks_2016
与FasterRCNN那种基于区域的方法不同本文用卷积的方法计算位置
用于公开数据集的自然图像
Code is made publicly available at: https://github.com/daijifeng001/r-fcn.
our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.
11. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset_2015
利用多项式网络解决数据集过小的问题
In this work, a stacked DPN (S-DPN) algorithm is proposed to further improv the representation performance of the original DPN, and S-DPN is then applied to the task of textur feature learning for ultrasound based tumor classification with small dataset.
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