Due to the unpredictable location, fuzzy texture, and diverse shape, accurate segmentation of the kidney tumor in CT images is an important yet challenging task. To this end, we, in this paper, present a cascaded trainable segmentation model termed as Crossbar-Net. Our method combines two novel schemes: 1) we originally proposed the crossbar patches, which consists of two orthogonal non-squared patches (i.e., the vertical patch and horizontal patch). The crossbar patches are able to capture both the global and local appearance information of the kidney tumors from both the vertical and horizontal directions simultaneously. 2) With the obtained crossbar patches, we iteratively train two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded training manner. During the training, the trained sub-models are encouraged to become more focused on the difficult parts of the tumor automatically (i.e., mis-segmented regions). Specifically, the vertical (horizontal) sub-model is required to help segment the mis-segmented regions for the horizontal (vertical) sub-model. Thus, the two sub-models could complement each other to achieve the self-improvement until convergence. In the experiment, we evaluate our method on a real CT kidney tumor dataset which is collected from 94 different patients including 3500 CT slices. Compared with state-of-the-art segmentation methods, the results demonstrate the superior performance of our method on the Dice similarity coefficient, true positive fraction, centroid distance, and Hausdorff distance. Moreover, to exploit the generalization to other segmentation tasks, we also extend our Crossbar-Net to two related segmentation tasks: 1) cardiac segmentation in MR images and 2) breast mass segmentation in X-ray images, showing the promising results for these two tasks. Our implementation is released at https: github.com Qianyu1226 Crossbar-Net.
Obviously, it is not a good choice to directly apply the above methods, which are specially designed for segmenting one typical organ, to our task. So, besides proposing the crossbar patch, we also propose a new patch extraction method and a new cascaded boosting training style. The extraction method is termed as to reduce redundancy and avoid label imbalance. And for the cascaded boosting training style, as mentioned in Section , methods in @cite_9 @cite_16 @cite_3 have introduced boosting into their deep learning models. Both @cite_3 and @cite_16 boost within one network. @cite_9 introduce a multi-scale net model, in which the CNNs are added iteratively and trained in stages. Although achieving a promising result in object counting task, the whole model in @cite_9 is too dependent on the first net. Therefore, we adopt the idea of boosting and avoid the shortcomings of method in @cite_9 to design Crossbar-Net. In Crossbar-Net, we keep all sub-models equal and make the sub-models in each round complementary and benefit to each other.
@cite_9 @cite_16 @cite_3
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In this paper, we address the task of object counting in images. We follow modern learning approaches in which a density map is estimated directly from the input image. We employ CNNs and incorporate two significant improvements to the state of the art methods: layered boosting and selective sampling. As a result, we manage both to increase the counting accuracy and to reduce processing time. Moreover, we show that the proposed method is effective, even in the presence of labeling errors. Extensive experiments on five different datasets demonstrate the efficacy and robustness of our approach. Mean Absolute error was reduced by 20 to 35 . At the same time, the training time of each CNN has been reduced by 50 . Deep Convolutional Neural Networks (CNNs) have demonstrated excellent performance in image classification, but still show room for improvement in object-detection tasks with many categories, in particular for cluttered scenes and occlusion. Modern detection algorithms like Regions with CNNs (, 2014) rely on Selective Search (, 2013) to propose regions which with high probability represent objects, where in turn CNNs are deployed for classification. Selective Search represents a family of sophisticated algorithms that are engineered with multiple segmentation, appearance and saliency cues, typically coming with a significant run-time overhead. Furthermore, (, 2014) have shown that most methods suffer from low reproducibility due to unstable superpixels, even for slight image perturbations. Although CNNs are subsequently used for classification in top-performing object-detection pipelines, current proposal methods are agnostic to how these models parse objects and their rich learned representations. As a result they may propose regions which may not resemble high-level objects or totally miss some of them. To overcome these drawbacks we propose a boosting approach which directly takes advantage of hierarchical CNN features for detecting regions of interest fast. We demonstrate its performance on ImageNet 2013 detection benchmark and compare it with state-of-the-art methods. We describe and analyze a new boosting algorithm for deep learning called SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct ensembles of classifiers, SelfieBoost boosts the accuracy of a single network. We prove a @math convergence rate for SelfieBoost under some "SGD success" assumption which seems to hold in practice.