Computational Mathematics Modeling in Cancer Analysis : First International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / edited by Wenjian Qin, Nazar Zaki, Fa Zhang, Jia Wu, Fan Yang.

Format:

Book

Contributor:

Qin, Wenjian., Editor.

Zaki, Nazar., Editor.

Zhang, Fa., Editor.

Wu, Jia, Editor.

Yang, Fan., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science 1611-3349 ; 13574

Lecture Notes in Computer Science, 1611-3349 ; 13574

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Computer engineering.

Computer networks.

Machine learning.

Computers.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Engineering and Networks.

Machine Learning.

Computing Milieux.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Engineering and Networks.

Machine Learning.

Computing Milieux.

Physical Description:

1 online resource (X, 160 pages) : 59 illustrations, 56 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

This book constitutes the proceedings of the First Workshop on Computational Mathematics Modeling in Cancer Analysis (CMMCA2022), held in conjunction with MICCAI 2022, in Singapore in September 2022. Due to the COVID-19 pandemic restrictions, the CMMCA2022 was held virtually. DALI 2022 accepted 15 papers from the 16 submissions that were reviewed. A major focus of CMMCA2022 is to identify new cutting-edge techniques and their applications in cancer data analysis in response to trends and challenges in theoretical, computational and applied aspects of mathematics in cancer data analysis.

Contents:

Cellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma

Is More Always Better? Effects of Patch Sampling in Distinguishing Chronic Lymphocytic Leukemia from Transformation to Diffuse Large B-cell Lymphoma

Repeatability of Radiomic Features against Simulated Scanning Position Stochasticity across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-Institutional Study on Head-and-Neck Cases

MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated Attention Mechanism

NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images

Self-supervised learning based on a pre-trained method for the subtype classification of spinal tumors

CanDLE: Illuminating Biases in Transcriptomic Pan-Cancer Diagnosis

Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns

Modality-collaborative AI model Ensemble for Lung Cancer Early Diagnosis

Clustering-based Multi-instance Learning Network for Whole Slide Image Classification

Multi-task Learning-driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification

Light Annotation Fine Segmentation: Histology Image Segmentation based on VGG Fusion with Global Normalisation CAM

Tubular Structure-Aware Convolutional Neural Networks for Organ at Risks Segmentation in Cervical Cancer Radiotherapy

Automatic Computer-aided Histopathologic Segmentation for Nasopharyngeal Carcinoma using Transformer Framework

Accurate Breast Tumor Identification UsingComputational Ultrasound Image Features.

Other Format:

Printed edition:

ISBN:

978-3-031-17266-3

9783031172663

Access Restriction:

Restricted for use by site license.