[MICCAI'23] Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train

[MICCAI2023 Challenge] Liver Lesion Diagnosis Challenge on Multi-phase MRI

MICCAI 2023: DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

Official code for Robust T-Loss for Medical Image Segmentation (MICCAI 2023)

CL-Detection2023 Challenge Official Repository

CL-Detection2023 Challenge Official Repository

MICCAI2023 - TransNuSeg: A Lightweight Multi-Task Transformer for Nuclei Segmentation

MICCAI 2023: Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images

Official implementation of "ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising"

MICCAI2023: Artifact Restoration in Histology Images with Diffusion Probabilistic Models

OCELOT 2023: Cell Detection from Cell-Tissue Interaction

"Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding" (MICCAI 2023)

Official repository of MICCAI 2023 paper: Distilling BlackBox to Interpretable models for Efficient Transfer Learning

Official repository for the paper "ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography" in MICCAI 2023 Conference

[MICCAI 2023] "How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?" by Gregory Holste et al.

The souce code of MICCAI'23 paper: Combat Long-tails in Medical Classification with Relation-aware Consistency and Virtual Features Compensation

MICCAI2023 -- Adjustable Robust Transformer for High Myopia Screening in Optical Coherence Tomography

Official PyTorch Code of our MICCAI2023 paper OpenAL

Repository of the paper "Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation", presented at MICCAI 2023.

This GitHub repository hosts the notebooks and tools developed as part of this thesis to automate the extraction, processing, and analysis of data from the MICCAI 2023 conference, aiding in the systematic review and providing a structured foundation for further research in this crucial area.