MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention

今年非常有幸参加了在深圳举办的MICCAI2019，特将部分会议内容总结如下：

Begin: 1998

构成：CVRMed（计算机视觉，虚拟现实和医学机器人），

MRCAS（医学机器人和计算机辅助手术）

VBC（生物医学可视化）

MIC & CAI

参会人员： 2000年 400人左右；

2017年 > 1000人；

2019年 >2300人；

KeyNote:

Keynote 1: AI Medical Imaging in China: Now and Future

Keynote 2 : AI Medical Imaging in China: Now and Future

Keynote 3 : Psychoradiology: The Forefront of Clinical Neuroimaging

Papers Overview:

45篇MRI，30篇CT，18篇Ultrasound，7篇OCT，5篇PET
21篇surgery
12篇CNN，19篇GAN，28篇Generative，93篇 Deep，150篇 Network
......

Loss Functions: Cross Entropy Loss + Dice Loss. Pixel-wise Reweighted Dice (PRD) loss. Image-wise Shape Regularization (ISR) loss. KL Divergence loss. Reconstruction loss. Domain loss. Focal loss. Mesh loss + Point loss. Dense CRF loss. ......

Segmentation Metrics: Dice Coefficient (DC) Mean Absolute Surface Distance (MAD) Mean Absolute Error (MAE) Hausdorff Distance (HD) ACC, AUC SE SP ......

Difficulty and Challenges:

3D Image: limitation of GPU memory & lack of pre-trained models & difficulty in training.
The lack of annotated data.
The image quality: low SNR & poor contrast.
High class imbalance (mainly background).
Significant variations in the shape and appearance of object.
Small target with large shape variations.
Hard Cases (Unclear boundary, big or small tumors).
Missing or ambiguous boundaries.
Inhomogeneous appearance and image quality.
......

Some Conclusion:

In 2019, the U-Net is still state of the art.
nnU-Net is a strong U-Net implementation that generalizes well to new datasets.
3D U-Net is a strong baseline for segmentation.
Cascaded pipline is an effective way to improve tumor segmentation performance.
Modified ground truth masks may lead to better model generalization.
High realism is not equivalent to meaningful features that improve classification performance.
Jointly learning registration and segmentation is superior over separate learning.
TL (Transfer Learning) should be applied for performance improvement.
MTL (Multi-Task Learning) is goog fit for small data size.
Coarse to fine approach is important.
Multi-View method.
Shape and Spatial Priors are important for performance improvement.
......

Other Research:

Prediction of Hemorrhagic Transformation in Acute Stroke (MRI) .
Color Normalization of H&E (Hematoxylin and Eosin) Stained Images.
Quality Evaluation for Noisy-Labeled Image Segmentation.
Early Prediction of ALzheimer's Disease (MRI).
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples.
Weakly supervision with image-level annotation.
Data Augmented Samples in Deep Learning.
Domain Adaptation Approach to Classification.
Curriculum semi-supervised segmentation.
Multi-View fusion of 4D ultrasound reveals fetal head.
Image generation.
Improve labeling efficiency.
Image enhancement.
Text to Mask Label.
Hyper Graph Learning
Bone Age Assessment
3D + 2D
......

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