Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor malignancy.MRI images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging.Furthermore,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori knowledge.This paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI images.During the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been constructed.Instead of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and Flair.These aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each stage.In the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while downsampling.In the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM outputs.The proposed method has been evaluated on datasets from the MICCAI BraTS2021 Challenge.The results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.

Designing Clinical Studies That Take into Account Traditional East Asian Medicine's Systems and Methods-with Focus on Pattern Identification

This article is based on two presentations held at Chinese medicine conference in Rothenburg （2013）, Germany and at the 19th Anniversary of Korean Institute of Oriental Medicine （KIOM） International Symposium of Current Research Trends in Traditional Medicine - Pattern of Identification （2013）. In designing clinical studies, it is a research question that leads to appropriate study design. However, they are mostly diagnostic procedures and techniques that are the key points to reflect the application of systems and methods in all forms of medicine - traditional East Asian medicine （TEAM） is no exception. The challenges within TEAM based on research reflect in different systems of medicine/theories such as traditional Japanese acupuncture, traditional Korean acupuncture and traditional Chinese acupuncture. This diversitv of medical svstems and methods applied in East Asia seems to have beenfruitful within the different countries and traditional medicines have found their places within the respective countries health systems. The existing diversity, from a clinician＇s point of view, may be viewed as a treasure when dealing with patients in the ＇real world＇. On the other hand, this diversity seems to challenge the scientific mind worldwide, esoeciallv when it comes to research. Hence. there is a and between clinical practice and research.