Large-scale gastric cancer screening and localization using multi-task deep neural network

HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation

Accurate medical image segmentation is of great significance for subsequent diagnosis and analysis. The acquisition of multi-scale information plays an important role in segmenting regions of interest of different sizes. With the emergence of Transformers, numerous networks adopted hybrid structures incorporating Transformers and CNNs to learn multi-scale information. However, the majority of research has focused on the design and composition of CNN and Transformer structures, neglecting the inconsistencies in feature learning between Transformer and CNN. This oversight has resulted in the hybrid network's performance not being fully realized. In this work, we proposed a novel hybrid multi-scale segmentation network named HmsU-Net, which effectively fused multi-scale features. Specifically, HmsU-Net employed a parallel design incorporating both CNN and Transformer architectures. To address the inconsistency in feature learning between CNN and Transformer within the same stage, we proposed the multi-scale feature fusion module. For feature fusion across different stages, we introduced the cross-attention module. Comprehensive experiments conducted on various datasets demonstrate that our approach surpasses current state-of-the-art methods.

Kernel Attention Transformer for Histopathology Whole Slide Image Analysis and Assistant Cancer Diagnosis

Transformer has been widely used in histopathology whole slide image analysis. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits its effectiveness and efficiency when applied to gigapixel histopathology images. In this paper, we propose a novel kernel attention Transformer (KAT) for histopathology WSI analysis and assistant cancer diagnosis. The information transmission in KAT is achieved by cross-attention between the patch features and a set of kernels related to the spatial relationship of the patches on the whole slide images. Compared to the common Transformer structure, KAT can extract the hierarchical context information of the local regions of the WSI and provide diversified diagnosis information. Meanwhile, the kernel-based cross-attention paradigm significantly reduces the computational amount. The proposed method was evaluated on three large-scale datasets and was compared with 8 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI analysis and is superior to the state-of-the-art methods.

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

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.

Effect of Combining Early Chemotherapy with Zhipu Liujunzi Decoction under the Concept of Strengthening and Consolidating Body Resistance for Gastric Cancer Patients and Nursing Strategy

Objective

To explore the clinical efficacy of combining early chemotherapy with Zhipu Liujunzi decoction under the concept of strengthening and consolidating body resistance for gastric cancer patients and nursing strategy.

Methods

The clinical data of 100 patients undergoing radical gastrectomy in our hospital from July 2019 to July 2020 were selected for the retrospective analysis, and the patients were divided into the control group and experimental group according to different treatment methods, with 50 cases in each group. Early chemotherapy after surgery was given to patients in the control group, and on the basis of the aforesaid treatment and under the concept of strengthening and consolidating body resistance, patients in the experimental group took Zhipu Liujunzi decoction and received the nursing strategy, so as to compare their effective rate, adverse reaction rate (ARR), immune function indicators, KPS scores, and nursing satisfaction scores.

Results

After treatment, the experimental group obtained significantly higher objective remission rate (ORR) and disease control rate (DCR) (P < 0.05), lower carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels (P < 0.001), higher immune parameters levels (P < 0.001), higher KPS scores and lower TCM symptom scores (P < 0.001), lower PSQI scores, SAS scores, and SDS scores (P < 0.001) and higher nursing satisfaction scores (P < 0.001), and lower total accidence rate of toxic side effects (P < 0.05) than the control group.

Conclusion

Under the concept of strengthening and consolidating body resistance, combining early chemotherapy with Zhipu Liujunzi decoction is a reliable method for improving the immune function and quality of life for gastric cancer patients with higher safety. Such a strategy greatly reduces the tumor marker levels in patients. Further research will be conducive to establishing a better solution for gastric cancer patients.