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Computational Mathematics Modeling in Cancer Analysis

First International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

  • Conference proceedings
  • © 2022

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Editors:
  1. Wenjian Qin

    1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

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  2. Nazar Zaki

    1. United Arab Emirates University, Al Ain, United Arab Emirates

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  3. Fa Zhang

    1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

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  4. Jia Wu

    1. The University of Texas MD Anderson Cancer Center, Houston, USA

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  5. Fan Yang

    1. Stanford University, Palo Alto, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13574)

Included in the following conference series:

Conference proceedings info: CMMCA 2022.

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About this book

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.

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Keywords

Table of contents (15 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

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

    • Pingjun Chen, Maliazurina B. Saad, Frank R. Rojas, Morteza Salehjahromi, Muhammad Aminu, Rukhmini Bandyopadhyay et al.
    Pages 1-10

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

    • Rukhmini Bandyopadhyay, Pingjun Chen, Siba El Hussein, Frank R. Rojas, Kingsley Ebare, Ignacio I. Wistuba et al.
    Pages 11-20

  4. 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

    • Jiang Zhang, Saikit Lam, Xinzhi Teng, Yuanpeng Zhang, Zongrui Ma, Francis Lee et al.
    Pages 21-34

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

    • Baorong Shi, Xinyu Liu, Fa Zhang
    Pages 35-46

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

    • Ahmad Obeid, Taslim Mahbub, Sajid Javed, Jorge Dias, Naoufel Werghi
    Pages 47-57

  7. Self-supervised Learning Based on a Pre-trained Method for the Subtype Classification of Spinal Tumors

    • Menglei Jiao, Hong Liu, Zekang Yang, Shuai Tian, Hanqiang Ouyang, Yuan Li et al.
    Pages 58-67

  8. CanDLE: Illuminating Biases in Transcriptomic Pan-Cancer Diagnosis

    • Gabriel Mejía, Natasha Bloch, Pablo Arbelaez
    Pages 68-77

  9. Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns

    • Xiaoxi Pan, Hanyun Zhang, Anca-Ioana Grapa, Khalid AbdulJabbar, Shan E Ahmed Raza, Ho Kwan Alvin Cheung et al.
    Pages 78-90

  10. Modality-Collaborative AI Model Ensemble for Lung Cancer Early Diagnosis

    • Wanxing Xu, Yinglan Kuang, Lin Wang, Xueqing Wang, Qiaomei Guo, Xiaodan Ye et al.
    Pages 91-99

  11. Clustering-Based Multi-instance Learning Network for Whole Slide Image Classification

    • Wei Wu, Zhonghang Zhu, Baptiste Magnier, Liansheng Wang
    Pages 100-109

  12. Multi-task Learning-Driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification

    • Jiayuan Zhu, Shujun Wang, Jinzheng He, Carola-Bibiane Schönlieb, Lequan Yu
    Pages 110-120

  13. Light Annotation Fine Segmentation: Histology Image Segmentation Based on VGG Fusion with Global Normalisation CAM

    • Yilong Li, Yaqi Wang, Le Dong, Juan Ye, Linyan Wang, Ruiquan Ge et al.
    Pages 121-130

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

    • Xinran Wu, Ming Cui, Yuhua Gao, Deyu Sun, He Ma, Erlei Zhang et al.
    Pages 131-140

  15. Automatic Computer-Aided Histopathologic Segmentation for Nasopharyngeal Carcinoma Using Transformer Framework

    • Songhui Diao, Luyu Tang, Jiahui He, Hanqing Zhao, Weiren Luo, Yaoqin Xie et al.
    Pages 141-149

  16. Accurate Breast Tumor Identification Using Computational Ultrasound Image Features

    • Yongqing Li, Wei Zhao
    Pages 150-158

  17. Back Matter

    Pages 159-160
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Other volumes

  1. Computational Mathematics Modeling in Cancer Analysis

Editors and Affiliations

  • Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Wenjian Qin

  • United Arab Emirates University, Al Ain, United Arab Emirates

    Nazar Zaki

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Fa Zhang

  • The University of Texas MD Anderson Cancer Center, Houston, USA

    Jia Wu

  • Stanford University, Palo Alto, USA

    Fan Yang

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