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Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE) , Global–Local Attention based Encoder (GLAE) , and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer [ABSTRACT FROM AUTHOR]

3D shapes and 2D images usually contain complementary information for each other, and thus joint analysis of both of them will benefit some problems existing in different domains. Leveraging the connection between 2D images and 3D shapes, it's potential to mine lacking information of one modal from the other. Stemming from this insight, we design and implement a CNN architecture to jointly analyze shapes and images even with few training data guidance. The core of our architecture is a domain adaptation algorithm, which builds up the connection between underlying feature spaces of images and shapes, then aligns and correlates the intrinsic structures therein. The proposed method facilitates the recognition and retrieval tasks. Experiments on the shape recognition tasks show that our approach has superior performance under the difficult setting: zero-shot learning and few-shot learning. We also evaluate our method on the retrieval tasks, and demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example, the high distinction among people, size of bladder variation and noise interference. Based on the above issues, we propose an MD-Unet network structure, which uses multi-scale images as the input of the network, and combines max-pooling with dilated convolution to increase the receptive field of the convolutional network. The results show that the proposed network can obtain higher precision than the existing models for the bladder cancer dataset. The MD-Unet can achieve state-of-art performance compared with other methods. [ABSTRACT FROM AUTHOR]