Learn Image Object Co-segmentation with Multi-scale Feature Fusion

© 2019 IEEE. Image object co-segmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multi-scale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intra-image and the inter-image features. Then these two kinds of features are further fused at each scale as the multi-scale co-features of common objects, and finally the multi-scale co-features are summed up and upsampled to obtain the co-segmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-The-Art co-segmentation methods while the computation cost has been effectively reduced

Image Co-saliency Detection and Co-segmentation from The Perspective of Commonalities

University of Technology Sydney. Faculty of Engineering and Information Technology.Image co-saliency detection and image co-segmentation aim to identify the common salient objects and extract them in a group of images. Image co-saliency detection and image co-segmentation are important for many content-based applications such as image retrieval, image editing, and content aware image/video compression. The image co-saliency detection and image co-segmentation are very close works. The most important part in these two works is the definition of the commonality of the common objects. Usually, common objects share similar low-level features, such as appearances, including colours, textures shapes, etc. as well as the high-level semantic features. In this thesis, we explore the commonalities of the common objects in a group of images from low-level features and high-level features, and the way to achieve the commonalities and finally segment the common objects. Three main works are introduced, including an image co-saliency detection model and two image co-segmentation methods. , an image co-saliency detection model based on region-level fusion and pixel-level refinement is proposed. The commonalities between the common objects are defined by the appearance similarities on the regions from all the images. It discovers the regions that are salient in each individual image as well as salient in the whole image group. Extensive experiments on two benchmark datasets demonstrate that the proposed co-saliency model consistently outperforms the state-of-the-art co-saliency models in both subjective and objective evaluation. , an unsupervised images co-segmentation method via guidance of simple images is proposed. The commonalities are still defined by hand-crafted features on regions, colours and textures, but not calculated among regions from all the images. It takes advantages of the reliability of simple images, and successfully improves the performance. The experiments on the dataset demonstrate the outperformance and robustness of the proposed method. , a learned image co-segmentation model based on convolutional neural network with multi-scale feature fusion is proposed. The commonalities between objects are not defined by handcraft features but learned from the training data. When training a neural network with multiple input images simultaneously, the resource cost will increase rapidly with the inputs. To reduce the resource cost, reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-the-art methods while the network has successfully gotten simplified and the resources cost is reduced

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.Comment: 12 pages, 5 figures. MICCAI Brats Challenge 201

CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images

Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality masks, so many studies now use imagelevel labels to achieve pixel-level segmentation to reduce the need for fine-grained annotation. To solve this problem, we propose an attention-based cross-view feature consistency end-to-end pseudo-mask generation framework named CVFC based on the attention mechanism. Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode. After a large number of experiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on the WSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, and OEEM, respectively.Comment: Submitted to BIBM202