Learning-Based Patch-Wise Metal Segmentation with Consistency Check

Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans

Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the outp…

Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising

The acquisition conditions for low-dose and high-dose CT images are usually different, so that the shifts in the CT numbers often occur. Accordingly, unsupervised deep learning-based approaches, which learn the target image distribution, often introduce CT number distortions and result in detrimental effects in diagnostic performance. To address this, here we propose a novel unsupervised learning approach for lowdose CT reconstruction using patch-wise deep metric learning. The key idea is to learn embedding space by pulling the positive pairs of image patches which shares the same anatomical s…

Where is the Fake? Patch-Wise Supervised GANs for Texture Inpainting

We tackle the problem of texture inpainting where the input images are textures with missing values along with masks that indicate the zones that should be generated. Many works have been done in image inpainting with the aim to achieve global and local consistency. But these works still suffer from limitations when dealing with textures. In fact, the local information in the image to be completed needs to be used in order to achieve local continuities and visually realistic texture inpainting. For this, we propose a new segmentor discriminator that performs a patch-wise real/fake classificati…

Patch-CNN：从最小扩散协议中训练数据高效的高保真扩散张量估计深度学习

Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols

Patch-CNN 提出一种使用最小 3×3×3 卷积核的神经网络，仅需单个受试者训练即可从六方向 DWI 中同时改善扩散张量标量参数和纤维方向估计，从而生成更优的纤维束图。

用于准确自动渗出物检测的全局与分块对比损失

A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate Detection

针对硬性渗出物形状不一、边界模糊的分割难题，提出融合分块密度对比与边缘检查模块的监督对比学习框架，在IDRiD上验证了其检测性能。

Bias-based Universal Adversarial Patch Attack for Automatic Check-out

Adversarial examples are inputs with imperceptible perturbations that easily misleading deep neural networks(DNNs). Recently, adversarial patch, with noise confined to a small and localized patch, has emerged for its easy feasibility in real-world scenarios. However, existing strategies failed to generate adversarial patches with strong generalization ability. In other words, the adversarial patches were input-specific and failed to attack images from all classes, especially unseen ones during training. To address the problem, this paper proposes a bias-based framework to generate class-agnost…