Titlebook: Computational Mathematics Modeling in Cancer Analysis; First International Wenjian Qin,Nazar Zaki,Fan Yang Conference proceedings 2022 The

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Self-supervised Learning Based on a Pre-trained Method for the Subtype Classification of Spinal Tum tumor subtypes from medical images in the early stage is of great clinical significance. Due to the complex morphology and high heterogeneity of spinal tumors, it can be challenging to diagnose subtypes from medical images accurately. In recent years, a number of researchers have applied deep learn

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,CanDLE: Illuminating Biases in Transcriptomic Pan-Cancer Diagnosis, task could be a valuable support in clinical practice and provide insights into the cancer causal mechanisms. To correctly approach this problem, the largest existing resource (The Cancer Genome Atlas) must be complemented with healthy tissue samples from the Genotype-Tissue Expression project. In

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,Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns,terns in routine histology samples is challenging due to the complexity of patterns and high intra-class variability. In this paper, we present a novel model with a multi-stream architecture, Cross-Stream Interactions (CroSIn), which fully considers crucial interactions across scales to gather abund

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,Light Annotation Fine Segmentation: Histology Image Segmentation Based on VGG Fusion with Global Noonsuming. To reduce the manual annotation workload, we propose a light annotation-based fine-level segmentation approach for histology images based on a VGG-based Fusion network with Global Normalisation CAM. The experts are only required to provide a rough segmentation annotation on the images, and

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,Tubular Structure-Aware Convolutional Neural Networks for Organ at Risks Segmentation in Cervical C are called Organ at Risks (OARs), which are prone to irreversible damage during radiotherapy. Therefore, accurate delineation of OARs is a critical step in ensuring radiotherapy dosimetry accuracy. However, currently existing deep learning-based cervical cancer OARs segmentation methods do not make

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Accurate Breast Tumor Identification Using Computational Ultrasound Image Features,. Ultrasound plays a key role and yet provides an economical solution for breast cancer screening. While valuable, ultrasound is still suffered from limited specificity, and its accuracy is highly related to the clinicians, resulting in inconsistent diagnosis. To address the challenge of limited spe