Tabla de Contenidos: “…Forth2, A.S. Olesen3, L. Dixon4, T.B. Rasmussen5, G. Cackett6, F. Werner6, A. Karger7, G. …”

Tabla de Contenidos: “…Leonardi' Paleontology and Prehistory Museum in Italy (A) and in Ferrara (B). View of the Quadrivio degli Angeli and location of Palace Turchi di Bagno (C). …”

Ver edición digitalizada de Google Books (Tomo 1)
Ver edición digitalizada de Google Books (Tomo 2)
Ver edición digitalizada de HathiTrust (Tomo 3)
Ver edición digitalizada de HathiTrust (Tomo 4)

“…Una línea es longitud sin anchura"?$1ґ (B, sino que los traduce a diagramas y símbolos coloridos. …”
Accés lliure
Ressenya:

Tabla de Contenidos: “…Front Cover -- State of the Art in Neural Networks and Their Applications -- Copyright Page -- Dedication -- Contents -- List of contributors -- About the editors -- Acknowledgments -- 1 Microscopy Cancer Cell Imaging in B-lineage Acute Lymphoblastic Leukemia -- 1.1 Introduction -- 1.2 Building a computer-assisted solution -- 1.3 Data preparation -- 1.3.1 Preparation of slide for microscopic imaging -- 1.3.2 Capture of microscopic images from healthy and cancer subjects for B-acute lymphoblastic leukemia cancer -- 1.4 Normalization of color stain to correct for abnormalities during the staining process -- 1.4.1 Quantitative results -- 1.5 Segmentation of cells of interest (in B-lineage ALL cancer) -- 1.5.1 Method-1 of cell segmentation using traditional image processing techniques -- 1.5.2 Method-2 of cell segmentation using deep belief network -- 1.5.3 Method-3 of cell segmentation using novel convolutional neural network architecture -- 1.5.3.1 Brief review of convolutional neural network architectures -- 1.5.3.2 Semantic versus instance segmentation in medical imaging -- 1.5.3.3 Method-3: novel proposed EDNiS-Net convolutional neural network for automated nuclei instance segmentation -- 1.5.3.3.1 Base module -- 1.5.3.3.2 Encoder module -- 1.5.3.3.3 Decoder module -- 1.5.3.3.4 Proposed loss function -- 1.5.3.3.5 Results and discussion -- 1.5.3.4 Region-proposal based convolutional neural network architectures -- 1.6 Classification of cancer and healthy cells -- 1.6.1 C-NMC 2019 challenge dataset -- 1.6.2 Classification on C-NMC 2019 dataset -- 1.6.3 SDCT-AuxNetθ CNN architecture for C-NMC 2019 dataset -- 1.7 Conclusions -- References -- 2 Computational imaging applications in brain and breast cancer -- 2.1 Introduction -- 2.2 Building upon current clinical standards -- 2.2.1 Clinical standards -- 2.2.2 Tissue segmentation…”