Results 111 to 120 of about 682 (137)

Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and -zero breast cancers: a multicenter study. [PDF]

Breast Cancer Res
Chen H, Liu Y, Zhao J, Jia X, Chai F, Peng Y, Hong N, Wang S, Wang Y.   +8 more
europepmc   +1 more source

Fast Automatic Fuzzy C-Means Knitting Pattern Color-Separation Algorithm Based on Superpixels. [PDF]

Sensors (Basel)
Ru X, Chen R, Peng L, Shi W.
europepmc   +1 more source

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation. [PDF]

Sci Rep
Chen Z, Yao L, Liu Y, Han X, Gong Z, Luo J, Zhao J, Fang G.   +7 more
europepmc   +1 more source

Ophthalmic Segmentation and Analysis Software (OASIS): A Comprehensive Tool for Quantitative Evaluation of Meibography Images. [PDF]

Transl Vis Sci Technol
Joseph N, Shivade V, Chen J, Marshall I, Avery E, Jennings D, Menegay H, Ramamirtham R, Wilson D, Benetz BA, Stokkermans T.   +10 more
europepmc   +1 more source

Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables. [PDF]

Sci Rep
Gupta A, Chug A, Singh AP.
europepmc   +1 more source

Structural-prior guided and feature-enhanced transformer with masked image modeling pretraining for retinal layers and fluid segmentation in macular edema OCT images. [PDF]

Biomed Opt Express
Wang S, Feng S, Wang Z, Ji Z, Liu J, Chen W, Fu B, Liu R, Chen W, Dai Y, Li C.   +10 more
europepmc   +1 more source

Dynamic graph convolution with comprehensive pruning and GNN classification for precise lymph node metastasis detection. [PDF]

Sci Rep
H N C, N S, S AR, Singh C, Y R.
europepmc   +1 more source

CT-based intratumoral heterogeneity quantification fusing deep learning radiomics for predicting lymph node metastasis in early-stage lung adenocarcinoma: a multicenter study. [PDF]

Sci Rep
Hu M, Hu X, Chen W, Xia C, Yao W, Zuo M, Lin Z, Deng L, Wu Z.   +8 more
europepmc   +1 more source

Automated error localisation and correction techniques for deep- learning-based segmentation of 3D MRI sequences based on feature- derived-region aggregation

Ruckli AC, Roesler V, Hess H, Meier MK, Schmaranzer F, Gerber N, Steppacher SD, Gerber K.   +7 more
europepmc   +1 more source