Yuhui Guo, Tongtong Li, Ziyang Zhao, Qi Sun, Miao Chen, Yanli Jiang, Zhijun Yao, Bin Hu
Liver fibrosis automatic diagnosis utilizing dense-fusion attention contrastive learning network
Medical physics
paper presents normalized standard errorbased statistical data binning method termed bin size index bsi yields optimized objective bin size constructing rational histogram facilitate subsequent deconvolution multimodal datasets materials characterization hence determination underlying probability density functions totally ten datasets including four normallydistributed synthetic ones three normallydistributed ones elasticity rocks obtained statistical nanoindentation three lognormallydistributed ones particle size distributions flocculated clay suspensions used illustrate bsis concepts algorithms results synthetic datasets prove methods accuracy effectiveness analyses real datasets materials characterization measurement demonstrate rationale performance applicability practical problems bsi method also enables determination number modes via comparative evaluation errors returned different trial bin sizes accuracy performance bsi method compared widely used binning methods former yields highest bsi smallest normalized standard errors new method particularly penalizes overfitting tends yield many pseudomodes via normalizing errors number modes hidden datasets also eliminates difficulty specifying criteria acceptable values fitting errors advantages disadvantages new method also discussed