Automatic retinal vessel segmentation is important for the diagnosis and prevention of ophthalmic diseases. The existing deep learning retinal vessel segmentation models always treat each pixel equally. However, the multi-scale vessel structure is a vital factor affecting the segmentation results, especially in thin vessels. To address this crucial gap, we propose a novel Fully Attention-based network (FANet) based on attention mechanisms to adaptively learn rich feature representation and aggregate the multi-scale information. Specifically, the framework consists of the image pre-processing procedure and the semantic segmentation networks. Green channel extraction (GE) and contrast limited adaptive histogram equalization (CLAHE) are employed as pre-processing to enhance the texture and contrast of retinal blood images. Besides, the network combines two types of attention modules with the U-Net. We propose a lightweight dual-direction attention block to model global dependencies and reduce intra-class inconsistencies, in which the weights of feature maps are updated based on the semantic correlation between pixels. The dual-direction attention block utilizes horizontal and vertical pooling operations to produce the attention map. In this way, the network aggregates global contextual information from semantic-closer regions or a series of pixels belonging to the same object category. Meanwhile, we adopt the selective kernel(SK) unit to replace the standard convolution for obtaining multi-scale features of different receptive field sizes generated by soft attention. Furthermore, we demonstrate that the proposed model can effectively identify irregular, noisy, and multi-scale retinal vessels. The abundant experiments on DRIVE, STARE, and CHASE_DB1 datasets show that our method achieves state-of-the-art performance.

Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficult to accurately explain the local characteristics of electrocardiogram (ECG) signals by manual analysis, due to their small amplitude and short duration, coupled with the complexity and non-linearity. Hence, in this paper, we propose a novel deep arrhythmia-diagnosis method named deep CNN-BLSTM network model to automatically detect the AF heartbeats using ECG signals. The model mainly consists of four convolution layers, two BLSTM layers, and two fully connected layers. The datasets of RR intervals (called set A.) and heartbeat sequences (P-QRS-T waves, called set B.) are fed into above-mentioned model. Most importantly, our proposed approach achieved favorable performances with an accuracy of 99.94%, 98.63% in the training and validation set of set A, respectively. In the testing set (unseen data sets), we obtained an accuracy of 96.59%, a sensitivity of 99.93%, and a specificity of 97.03%. To the best of our knowledge, the algorithm we proposed has shown excellent results comparing to many state-of-art researches, which provides a new solution for AF automatic detection.

Automatic gastric cancer segmentation is a challenging problem in digital pathology image analysis. Accurate segmentation of gastric cancer regions can efficiently facilitate clinical diagnosis and pathological research. Technically, this problem suffers from various sizes, vague boundaries, and the nonrigid characters of cancerous regions. For addressing these challenges, we use a deep learning based method and integrate several customized modules. Structurally, we replace the basic form of convolution with deformable and Atrous convolutions in specific layers, for adapting to the non-rigid characters and larger receptive field. We take advantage of the Atrous Spatial Pyramid Pooling module and encoder-decoder based semantic-level embedding networks for multi-scale segmentation. Additionally, we propose a lightweight decoder to fuse the contexture information, and utilize the Dense Upsampling Convolution for boundary refinement at the end of the decoder. Experimentally, sufficient comparative experiments are enforced on our own gastric cancer segmentation dataset, which is delicately annotated to pixel-level by medical specialists. Quantitative comparisons against several prior methods demonstrate the superiority of our approach. We achieve 91.60% for pixel-level accuracy and 82.65% for mean Intersection over Union.

Remote sensing image segmentation is a challenging task in remote sensing image analysis. Remote sensing image segmentation has great significance in urban planning, crop planting, and other fields that need plentiful information about the land. Technically, this task suffers from the ultra-high resolution, large shooting angle, and feature complexity of the remote sensing images. To address these issues, we propose a deep learning-based network called ATD-LinkNet with several customized modules. Specifically, we propose a replaceable module named AT block using multi-scale convolution and attention mechanism as the building block in ATD-LinkNet. AT block fuses different scale features and effectively utilizes the abundant spatial and semantic information in remote sensing images. To refine the nonlinear boundaries of internal objects in remote sensing images, we adopt the dense upsampling convolution in the decoder part of ATD-LinkNet. Experimentally, we enforce sufficient comparative experiments on two public remote sensing datasets (Potsdam and DeepGlobe Road Extraction). The results show our ATD-LinkNet achieves better performance against most state-of-the-art networks. We obtain 89.0% for pixel-level accuracy in the Potsdam dataset and 62.68% for mean Intersection over Union in the DeepGlobe Road Extraction dataset.

Uveal melanoma is the most common primary intraocular malignancy in adults, with nearly half of all patients eventually developing metastases, which are invariably fatal. Manual assessment of the level of expression of the tumor suppressor BRCA1-associated protein 1 (BAP1) in tumor cell nuclei can identify patients with a high risk of developing metastases, but may suffer from poor reproducibility. In this study, we verified whether artificial intelligence could predict manual assessments of BAP1 expression in 47 enucleated eyes with uveal melanoma, collected from one European and one American referral center. Digitally scanned pathology slides were divided into 8176 patches, each with a size of 256 × 256 pixels. These were in turn divided into a training cohort of 6800 patches and a validation cohort of 1376 patches. A densely-connected classification network based on deep learning was then applied to each patch. This achieved a sensitivity of 97.1%, a specificity of 98.1%, an overall diagnostic accuracy of 97.1%, and an F1-score of 97.8% for the prediction of BAP1 expression in individual high resolution patches, and slightly less with lower resolution. The area under the receiver operating characteristic (ROC) curves of the deep learning model achieved an average of 0.99. On a full tumor level, our network classified all 47 tumors identically with an ophthalmic pathologist. We conclude that this deep learning model provides an accurate and reproducible method for the prediction of BAP1 expression in uveal melanoma.

With the development of deep convolutional neural networks, medical image segmentation has achieved a series of breakthroughs in recent years. However, high-performance convolutional neural networks always mean numerous parameters and high computation costs, which will hinder the applications in resource-limited medical scenarios. Meanwhile, the scarceness of large-scale annotated medical image datasets further impedes the application of high-performance networks. To tackle these problems, we propose Graph Flow, a comprehensive knowledge distillation framework, for both network efficiency and annotation-efficiency medical image segmentation. Specifically, the Graph Flow Distillation transfers the essence of cross-layer variations from a well trained cumbersome teacher network to a non-trained compact student network. In addition, an unsupervised Paraphraser Module is integrated to purify the knowledge of the teacher, which is also beneficial for the training stabilization. Furthermore, we build a unified distillation framework by integrating the adversarial distillation and the vanilla logits distillation, which can further refine the final predictions of the compact network. With different teacher networks (traditional convolutional architecture or prevalent transformer architecture) and student networks, we conduct extensive experiments on four medical image datasets with different modalities (Gastric Cancer, Synapse, BUSI, and CVC-ClinicDB).We demonstrate the prominent ability of our method on these datasets, which achieves competitive performances. Moreover, we demonstrate the effectiveness of our Graph Flow through a novel semi-supervised paradigm for dual efficient medical image segmentation. Our code will be available at Graph Flow .