In the domain of medical image analysis,there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly d...In the domain of medical image analysis,there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly due to their outstanding performance.This review article seeks to concisely explore the historical evolution,specific applications,and training methodologies associated with these large models considering their current prominence in medical image analysis.Moreover,we delve into the prevailing challenges and prospective opportunities related to the utilization of large models in the context of medical image analysis.Through a comprehensive analysis of these substantial models,this study aspires to provide valuable insights and guidance to researchers in the field of radiology,fostering further advances and optimizations in their incorporation into medical image analysis practices,in accordance with the submission requirements.展开更多
Physical testing of large-scale ship models at sea is a new experimental method.It is a cheap and reliable way to research the environment adaptability of a ship in complex and extreme wave conditions.It is necessary ...Physical testing of large-scale ship models at sea is a new experimental method.It is a cheap and reliable way to research the environment adaptability of a ship in complex and extreme wave conditions.It is necessary to have a stable experimental system for the test.Since the experimental area is large, a remote control system and a telemetry system are essential, and were designed by the authors.An experiment was conducted on the Songhuajiang River to test the systems.The relationship between the model's speed and its electromotor's revolutions was also measured during the model test.The results showed that the two systems make it possible to carry out large-scale model tests at sea.展开更多
INTRODUCTION In recent years,the development of large-scale foundationmodels(LFMs)has made great advances.However,the high training costs and computational demands have long been a bottleneck for the widespread adopti...INTRODUCTION In recent years,the development of large-scale foundationmodels(LFMs)has made great advances.However,the high training costs and computational demands have long been a bottleneck for the widespread adoption of this technology.With technological advancements,this situation is undergoing a fundamental transformation.The recent release of DeepSeek-V31 has sparked extensive discussions.Through innovative architectural design and efficient training strategies,it has significantly reduced training costswhile achieving performance comparable to top-tier closed-source models.The pre-training cost of DeepSeek-V3is only$5.576 million,far lower than the hundreds ofmillions of dollars required formodels like GPT-4.As shwon in Figure 1,this breakthrough not onlymarks the democratization of LFM technology but also opens up opportunities for more small-and medium-sized enterprises and research institutions to participate in AI innovation.In the future,LFMs will no longer be a game for the few.展开更多
基金funding from the National Key Research and Development Program of China under Grant Nos.2021YFC2500402,2017YFA0700401,2022YFC2503700,and 2022YFC2503705the Ministry of Science and Technology of China under Grant No.2017YFA0205200+1 种基金the National Natural Science Foundation of China under Grant Nos.82001917,81930053,82090052,62027901,81227901,92159202,U22A2023,U22A20343,and 82172039the Project of High-Level Talents Team Introduction in Zhuhai City(Zhuhai HLHPTP201703).
文摘In the domain of medical image analysis,there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly due to their outstanding performance.This review article seeks to concisely explore the historical evolution,specific applications,and training methodologies associated with these large models considering their current prominence in medical image analysis.Moreover,we delve into the prevailing challenges and prospective opportunities related to the utilization of large models in the context of medical image analysis.Through a comprehensive analysis of these substantial models,this study aspires to provide valuable insights and guidance to researchers in the field of radiology,fostering further advances and optimizations in their incorporation into medical image analysis practices,in accordance with the submission requirements.
基金Supported by the National Defense Foundation under Grant No.51414030204CB0109
文摘Physical testing of large-scale ship models at sea is a new experimental method.It is a cheap and reliable way to research the environment adaptability of a ship in complex and extreme wave conditions.It is necessary to have a stable experimental system for the test.Since the experimental area is large, a remote control system and a telemetry system are essential, and were designed by the authors.An experiment was conducted on the Songhuajiang River to test the systems.The relationship between the model's speed and its electromotor's revolutions was also measured during the model test.The results showed that the two systems make it possible to carry out large-scale model tests at sea.
基金supported by the National Natural Science Foundation of China under grant nos.62206266 and 62372430the Youth Innovation Promotion Association CAS no.2023112.
文摘INTRODUCTION In recent years,the development of large-scale foundationmodels(LFMs)has made great advances.However,the high training costs and computational demands have long been a bottleneck for the widespread adoption of this technology.With technological advancements,this situation is undergoing a fundamental transformation.The recent release of DeepSeek-V31 has sparked extensive discussions.Through innovative architectural design and efficient training strategies,it has significantly reduced training costswhile achieving performance comparable to top-tier closed-source models.The pre-training cost of DeepSeek-V3is only$5.576 million,far lower than the hundreds ofmillions of dollars required formodels like GPT-4.As shwon in Figure 1,this breakthrough not onlymarks the democratization of LFM technology but also opens up opportunities for more small-and medium-sized enterprises and research institutions to participate in AI innovation.In the future,LFMs will no longer be a game for the few.
