Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
This is a conceptual paper which was motivated by the fact that Swaziland does not have a Social Accounting Matrix （SAM） in place and as such there are many shocks that affect that country＇s economy but which canno...This is a conceptual paper which was motivated by the fact that Swaziland does not have a Social Accounting Matrix （SAM） in place and as such there are many shocks that affect that country＇s economy but which cannot be analyzed effectively. Most notable of this is the economic effects of the HIV/AIDS scourge that is affecting that country of which it has been difficult to determine the effects it has had on the economy in an objective manner. This paper will highlight the usefulness of the SAM and Computable General Equilibrium （CGE） models in analyzing the possible economic effects of HIV/AIDS in Swaziland. The absence of a SAM for Swaziland means that empirical analysis of the effect of the disease on the economy could not be undertaken, but it is hoped that the arguments presented here will contribute to the use of these methods as tools for analyzing various shocks in an economy. The paper is divided into 4 parts. Part 1 is a brief introduction into the Swaziland economy, part 2 is a brief description of the SAM, description of CGE Modeling and a detailed application of the SAM data into the CGE modeling framework, part 3 introduces the HIV/AIDS situation in Swaziland and models its possible effects using a macroeconomic SAM and part 4 is the discussion and conclusion. The main aim of the paper then is to lay the basic framework to help small developing countries develop practical SAMs that will become an important tool in analyzing the performances of their economies.展开更多
Senescence accelerated mice (SAMP8) have a shorter lifespan with deficits in learning and memory, compared with control SAMR1 mice (Takeda, Elsevier Science BV, 1994).
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
文摘This is a conceptual paper which was motivated by the fact that Swaziland does not have a Social Accounting Matrix （SAM） in place and as such there are many shocks that affect that country＇s economy but which cannot be analyzed effectively. Most notable of this is the economic effects of the HIV/AIDS scourge that is affecting that country of which it has been difficult to determine the effects it has had on the economy in an objective manner. This paper will highlight the usefulness of the SAM and Computable General Equilibrium （CGE） models in analyzing the possible economic effects of HIV/AIDS in Swaziland. The absence of a SAM for Swaziland means that empirical analysis of the effect of the disease on the economy could not be undertaken, but it is hoped that the arguments presented here will contribute to the use of these methods as tools for analyzing various shocks in an economy. The paper is divided into 4 parts. Part 1 is a brief introduction into the Swaziland economy, part 2 is a brief description of the SAM, description of CGE Modeling and a detailed application of the SAM data into the CGE modeling framework, part 3 introduces the HIV/AIDS situation in Swaziland and models its possible effects using a macroeconomic SAM and part 4 is the discussion and conclusion. The main aim of the paper then is to lay the basic framework to help small developing countries develop practical SAMs that will become an important tool in analyzing the performances of their economies.
文摘Senescence accelerated mice (SAMP8) have a shorter lifespan with deficits in learning and memory, compared with control SAMR1 mice (Takeda, Elsevier Science BV, 1994).
