MICCAI 2022 Paper with Codes

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Contents


1. Backbone

UNeXt: MLP-based Rapid Medical Image Segmentation Network

Spatial-Hierarchical Graph Neural Network with Dynamic Structure Learning for Histological Image Classification


2. Multi Task Learning

TGANet: Text-guided Attention for Improved Polyp Segmentation


3. Self Supervised Learning

mulEEG: A Multi-View Representation Learning on EEG Signals

Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Ray

Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions

Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image


4. Weakly Supervised Learning

Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

Transformer Based Multiple Instance Learning for Weakly Supervised Histopathology Image Segmentation

Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification

SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological Image Analysis


5. Semi Supervised Learning

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation


6. Imbalanced Data

Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification


7. Multi Modal

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency


8. Data Augmentation

SapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation

RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization

SUPER-IVIM-DC: Intra-voxel Incoherent Motion Based Fetal Lung Maturity Assessment from Limited DWI Data Using Supervised Learning Coupled with Data-Consistency


9. Knowledge-Distillation

Distilling Knowledge from Topological Representations for Pathological Complete Response Prediction


Inclusion Critertion

In this initial stage where MICCAI papers are not official published, we use the key world MICCAI 2022 on github to search the associated respoitory. Afterwards, we primary check if there is a pre-print version on arXiv.