Computational Mathematics Modeling in Cancer Analysis

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

AvWenjian Qin,Nazar Zaki

Häftad, Engelska, 2022

Del 13574 i serien Lecture Notes in Computer Science

605 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-09-20
Mått:155 x 235 x 10 mm
Vikt:271 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:160
Förlag:Springer International Publishing AG
ISBN:9783031172656

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Informationsteknik: allmänt inom Data och IT
Hårdvara inom Data och IT

Innehållsförteckning

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.