Deep learning approaches have demonstrated significant progress in breast cancer histopathological image diagnosis. Training an interpretable diagnosis model using high-resolution histopathological image is still challenging. To alleviate this problem, a novel multi-view attention-guided multiple instance detection network (MA-MIDN) is proposed. The traditional image classification problem is framed as a weakly supervised multiple instance learning (MIL) problem. We first divide each histopathology image into instances and form a corresponding bag to fully utilize high-resolution information through MIL. Then a new multiple-view attention (MVA) algorithm is proposed to learn attention on the instances from the image to localize the lesion regions in this image. A MVA-guided MIL pooling strategy is designed for aggregating instance-level features to obtain bag-level features for the final classification. The proposed MA-MIDN model performs lesion localization and image classification, simultaneously. Particularly, we train the MA-MIDN model under the deep mutual learning (DML) schema. This transfers DML to a weakly supervised learning problem. Three public breast cancer histopathological image datasets are chosen to evaluate classification and localization results. The experimental results demonstrate that the MA-MIDN model is superior to the latest baselines in terms of diagnosis accuracy, AUC, Precision, Recall, and F1. Notably, it achieves better localization results without compromising classification performance, thereby proving its higher practicality. The code for the MA-MIDN model is available at https://github.com/lcxlcx/MA-MIDN.

Retinal image is a crucial window for the clinical observation of cardiovascular, cerebrovascular, or other correlated diseases. Retinal vessel segmentation is of great benefit to the clinical diagnosis. Recently, the convolutional neural network (CNN) has become a dominant method in the retinal vessel segmentation field, especially the U-shaped CNN models. However, the conventional encoder in CNN is vulnerable to noisy interference, and the long-rang relationship in fundus images has not been fully utilized. In this paper, we propose a novel model called Transformer in M-Net (TiM-Net) based on M-Net, diverse attention mechanisms, and weighted side output layers to efficaciously perform retinal vessel segmentation. First, to alleviate the effects of noise, a dual-attention mechanism based on channel and spatial is designed. Then the self-attention mechanism in Transformer is introduced into skip connection to re-encode features and model the long-range relationship explicitly. Finally, a weighted SideOut layer is proposed for better utilization of the features from each side layer. Extensive experiments are conducted on three public data sets to show the effectiveness and robustness of our TiM-Net compared with the state-of-the-art baselines. Both quantitative and qualitative results prove its clinical practicality. Moreover, variants of TiM-Net also achieve competitive performance, demonstrating its scalability and generalization ability. The code of our model is available at https://github.com/ZX-ECJTU/TiM-Net.