Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist radiologists in reading images, but also provide image-guided clinical diagnosis and improve diagnosis accuracy. However, due to the inhomogeneous intensity distribution of gastric tumor and LN in CT scans, the ambiguous/missing boundaries, and highly variable shapes of gastric tumor, it is quite challenging to develop an automatic solution. To comprehensively address these challenges, we propose a novel 3D multi-attention guided multi-task learning network for simultaneous gastric tumor segmentation and LN classification, which makes full use of the complementary information extracted from different dimensions, scales, and tasks. Specifically, we tackle task correlation and heterogeneity with the convolutional neural network consisting of scale-aware attention-guided shared feature learning for refined and universal multi-scale features, and task-aware attention-guided feature learning for task-specific discriminative features. This shared feature learning is equipped with two types of scale-aware attention (visual attention and adaptive spatial attention) and two stage-wise deep supervision paths. The task-aware attention-guided feature learning comprises a segmentation-aware attention module and a classification-aware attention module. The proposed 3D multi-task learning network can balance all tasks by combining segmentation and classification loss functions with weight uncertainty. We evaluate our model on an in-house CT images dataset collected from three medical centers. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, and obtains promising performance for tumor segmentation and LN classification. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge. Our implementation is released at https://github.com/infinite-tao/MA-MTLN .

3D Multi-Attention Guided Multi-Task Learning Network for Automatic Gastric Tumor Segmentation and Lymph Node Classification

Automatic segmentation is a fundamental task in computer-assisted medical image analysis. Convolutional neural networks (CNNs) have been widely used for medical image segmentation tasks. Currently, most deep learning-based methods output a probability map and use a hand-crafted threshold to generate the final segmentation result, while how confident the network is of the probability map remains unclear. The segmentation result can be quite unreliable even though the probability is much higher than the threshold since the uncertainty of the probability can also be high. Moreover, boundary information loss caused by consecutive pooling layers and convolution strides makes the object’s boundary in segmentation even more unreliable. In this paper, we propose an uncertainty guided network referred to as UG-Net for automatic medical image segmentation. Different from previous methods, our UG-Net can learn from and contend with uncertainty by itself in an end-to-end manner. Specifically, UG-Net consists of three parts: a coarse segmentation module (CSM) to obtain the coarse segmentation and the uncertainty map, an uncertainty guided module (UGM) to leverage the obtained uncertainty map in an end-to-end manner, and a feature refinement module (FRM) embedded with several dual attention (DAT) blocks to generate the final segmentations. In addition, to formulate a unified segmentation network and extract richer context information, a multi-scale feature extractor (MFE) is inserted between the encoder and decoder of the CSM. Experimental results show that the proposed UG-Net outperforms the state-of-the-art methods on nasopharyngeal carcinoma (NPC) segmentation, lung segmentation, optic disc segmentation and retinal vessel detection. • A deep learning-based network named UG-Net is proposed for medical image segmentation. • An uncertainty guided module is proposed to learn from uncertainty in an end-to-end manner. • A feature refinement module with dual attention mechanism is designed for further performance promotion. • A multi-scale feature extractor is devised to fit into different segmentation tasks. 

Unified medical image segmentation by learning from uncertainty in an end-to-end manner

Segmentation information with attention integration for classification of breast tumor in ultrasound image

Multi-task deep learning for medical image computing and analysis: A review

/pdf/medical-image-segmentation-with-3d-convolutional-neural-1v2nt7bn.pdf

Medical image segmentation with 3D convolutional neural networks: A survey

Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images

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Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images