Medical Image Learning with Limited and Noisy Data : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings / edited by Zhiyun Xue, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru, Zhaohui Liang.

Format:

Book

Author/Creator:

Xue, Zhiyun.

Contributor:

Antani, Sameer.

Zamzmi, Ghada.

Yang, Feng.

Rajaraman, Sivaramakrishnan.

Huang, Sharon Xiaolei.

Linguraru, Marius George.

Liang, Zhaohui.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 14307

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (274 pages)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2023.

Summary:

This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.

Contents:

Efficient Annotation and Training Strategies

Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-quality Annotations

ScribSD: Scribble-supervised Fetal MRI Segmentation based on Simultaneous Feature and Prediction Self-Distillation

Label-efficient Contrastive Learning-based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images

Affordable Graph Neural Network Framework using Topological Graph Contraction

Approaches for Noisy, Missing, and Low Quality Data

Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT

A Multitask Framework for Label Refinement and Lesion Segmentation in Clinical Brain Imaging

COVID-19 Lesion Segmentation Framework for the Contrast-enhanced CT in the Absence of Contrast-enhanced CT Annotation

Feasibility of Universal Anomaly Detection without Knowingthe Abnormality in Medical Image

Unsupervised, Self-supervised, and Contrastive Learning

Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection

FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation

Masked Image Modeling for Label-Efficient Segmentation in Two-Photon Excitation Microscopy

Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning

SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction

Robust Unsupervised Image to Template Registration Without Image Similarity Los

A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue

Weakly-supervised, Semi-supervised, and Multitask Learning

Combining Weakly Supervised Segmentation with Multitask Learning forImproved 3D MRI Brain Tumour Classification

Exigent Examiner and Mean Teacher: An Advanced 3D CNN-based Semi-Supervised Brain Tumor Segmentation Framework

Extremely Weakly-supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation

Multi-Task Learning for Few-Shot Differential Diagnosis of Breast Cancer Histopathology Image

Active Learning

Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach

Test-time Augmentation-based Active Learning and Self-training for Label-efficient Segmentation

Active Transfer Learning for 3D Hippocampus Segmentation

Transfer Learning

Using Training Samples as Transitive Information Bridges in Predicted 4D MRI

To Pretrain or not to Pretrain? A Case Study of Domain-Specific Pretraining for Semantic Segmentation in Histopathology

Large-scale Pretraining on Pathological Images for Fine-tuning of Small Pathological Benchmarks.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Xue, Zhiyun Medical Image Learning with Limited and Noisy Data

ISBN:

9783031449178

3031449177

OCLC:

1402285499