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Akademický článek

DenseTrans: Multimodal Brain Tumor Segmentation Using Swin Transformer

Autor: Li ZongRen, Wushouer Silamu, Wang Yuzhen, Wei Zhe

Publikováno v: IEEE Access, Vol 11, Pp 42895-42908 (2023)

Aiming at the task of automatic brain tumor segmentation, this paper proposes a new DenseTrans network. In order to alleviate the problem that convolutional neural networks(CNN) cannot establish long-distance dependence and obtain global context info

Externí odkaz: https://doaj.org/article/2651c5ea258e40fbada6aa0624b0f039

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Akademický článek

Focal cross transformer: multi-view brain tumor segmentation model based on cross window and focal self-attention

Autor: Li Zongren, Wushouer Silamu, Feng Shurui, Yan Guanghui

Publikováno v: Frontiers in Neuroscience, Vol 17 (2023)

IntroductionRecently, the Transformer model and its variants have been a great success in terms of computer vision, and have surpassed the performance of convolutional neural networks (CNN). The key to the success of Transformer vision is the acquisi

Externí odkaz: https://doaj.org/article/0856e946bede4f62ba0bfd3d51551e2e

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UniCompress: Enhancing Multi-Data Medical Image Compression with Knowledge Distillation

Autor: Yang, Runzhao, Chen, Yinda, Zhang, Zhihong, Liu, Xiaoyu, Li, Zongren, He, Kunlun, Xiong, Zhiwei, Suo, Jinli, Dai, Qionghai

In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy enc

Externí odkaz: http://arxiv.org/abs/2405.16850

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CUTS+: High-dimensional Causal Discovery from Irregular Time-series

Autor: Cheng, Yuxiao, Li, Lianglong, Xiao, Tingxiong, Li, Zongren, Zhong, Qin, Suo, Jinli, He, Kunlun

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Gr

Externí odkaz: http://arxiv.org/abs/2305.05890

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CUTS: Neural Causal Discovery from Irregular Time-Series Data

Autor: Cheng, Yuxiao, Yang, Runzhao, Xiao, Tingxiong, Li, Zongren, Suo, Jinli, He, Kunlun, Dai, Qionghai

Publikováno v: The Eleventh International Conference on Learning Representations, Feb. 2023

Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most exis

Externí odkaz: http://arxiv.org/abs/2302.07458

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