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Image inpainting is a technique that uses information from known areas of an image to repair missing or damaged areas of the image.Image editing software based on it has made it easy to edit and modify the content of digital images without any specialized foundation.When image inpainting techniques are used to maliciously remove the content of an image, it will cause confidence crisis on the real image.Current researches in image inpainting forensics can only effectively detect a certain type of image inpainting.To address this problem, a passive forensic method for image inpainting was proposed, which is based on a two-branch network.The high-pass filtered convolutional network in the dual branch first used a set of high-pass filters to attenuate the low-frequency components in the image.Then features were extracted using four residual blocks, and two transposed convolutions were performed with 4x up-sampling to zoom in on the feature map.And thereafter a 5×5 convolution was used to attenuate the tessellation artifacts from the transposed convolutions to generate a discriminative feature map on the high-frequency components of the image.The dual-attention feature fusion branch in the dual branch first added a local binary pattern feature map to the image using a preprocessing block.Then the dual-attention convolution block was used to adaptively integrate the image’s local features and global dependencies to capture the differences in content and texture between the inpainted and pristine regions of the image.Additionally, the features extracted from the dual-attention convolution block were fused, and the feature maps were up-sampled identically to generate the discriminative image content and texture on the feature maps.The extensive experimental results show the proposed method improved the F1 score by 2.05% and the Intersection over Union(IoU) by 3.53% for the exemplar-based method and by 1.06% and 1.22% for the deep-learning-based method in detecting the inpainted region of the removed object.Visualization of the results shows that the edges of the removed objects can be accurately located on the detected inpainted area.

A medical image retrieval method combining attention mechanism is proposed for a series of problems such as poor retrieval performance,low accuracy and lack of interpretability in current medical image retrieval.Based on deep convolutional neural networks and taking Bayesian models as the framework,the proposed algorithm introduces an attention mechanism module guided by semantic features.Local feature descriptors containing semantic information are generated under the guidance of the classification network.Both global features and local features rich in semantic information are used as inputs to the hash network,which enhances the feature representation capability of hash coding by guiding the hash network to pay attention to important feature regions from both global and local perspectives.And the weighted likelihood estimation function is introduced to solve the problem of the unbalanced number of positive and negative sample pairs.MAP and NDCG are used as evaluation metrics,and the ChestX-ray14 dataset is selected for experiments.The proposed algorithm is compared with the current commonly used deep ha-shing methods.Experiment results show that the MAP and NDCG values are much better than the existing deep hashing methods at different code levels of hash coding,which proves the effectiveness of the proposed algorithm.

Objective To explore the clinicopathological and imaging features of cryptococcal granuloma in central nervous system (CNS), and review related literatures to enhance the recognition and diagnostic level. Methods Retrospectively analysed clinicopathological, histopathological and immunohistochemical features and imaging findings of one case of cryptococcal granuloma in the right frontal lobe. HE staining, alcian blue⁃periodic acid⁃Schiff (AB⁃PAS) staining, Gomori silvermethenamine (periodic acid silvermethenamine, PASM) staining, and immunohistochemistry staining were used respectively. CD68 was determined. The related literatures were reviewed. Results A 30⁃year⁃old male patient presented with sudden limb convulsion and accompanied with temporary loss of consciousness before 6 d. Cranial computed tomography (CT) showed a nodular hyperdense lesion in the right frontal lobe. Cranial magnetic resonance imaging (MRI) examination demonstrated an enhancing mass in the right frontal lobe area, measuring 3.10 cm × 2.80 cm × 1.50 cm in dimension, which showed low T2 ⁃ weighted signal intensity, contrasting with the surrounding hyperintense cerebral edema and irregular lobulated margin of the ring⁃enhancing lesion on T1WI. Grossly, grayish and (or) yellowish gray chunky tissues were found, measuring 2.80 cm × 2.50 cm × 1.50 cm. Pathological finding revealed a large number of cryptococcal lesions and granuloma formation. Final diagnosis was cryptococcal granuloma or cryptococcoma of the right frontal lobe. Conclusion Cryptococcosis is a subacute or chronic fungal disease caused by cryptococcus neoformans, mainly affects CNS and lungs. There are two main forms of infection of the CNS, which include meningitis (with or without parenchymal cysts) and cryptococcal abscess (cryptococcoma). Cryptococcal meningitis and meningoencephalitis are most common. Cryptococcal granuloma is rare. Cerebral cryptococcosis is often misdiagnosed, we should pay close attention to it. Early diagnosis and prompt treatment is crucial. DOI：10.3969/j.issn.1672-6731.2010.04.020