Comparing segmentation model on brain segmentation task
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Updated
May 8, 2023 - Python
Comparing segmentation model on brain segmentation task
Labelless automated airway measurement using style transfer to generate synthetic data.
Correlation Between IBSI Morphological Features and Manually-Annotated Shape Attributes on Lung Lesions at CT (MIUA 2022)
Dissertation completed for the award of MSci in Computer Science. This dissertation is about automated breast cancer detection in low-resolution whole-slide pathology images using a deep convolutional neural network pipeline.
Comprehensive Analysis of Domain Transferability Using Deep-Learning-Based Interpretation of Chest Radiographs
Official PyTorch implementation of the paper: <Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences>
IEEE ISBI 2022 paper: CEUSegNet: A Cross-Modality Lesion Segmentation Network for Contrast-Enhanced Ultrasound.
Official Repository of STS Challenges 😎
Foundation models based medical image analysis
Implemented Tiramisu network using pytorch.
Flexible federated learning enables institutions to jointly train deep learning models even when data is non-uniformly labeled. The resulting models are superior to models which are trained with conventional methods.
Building an AI model for chest X-ray under patient privacy guarantees
This project is an advanced AI-powered tool designed to analyze medical images, leveraging the robust capabilities of Google Gemini for accurate image recognition and Streamlit for an intuitive user interface.
[MICCAI 2024] Embracing Massive Medical Data
Keras Version of CenterNet for Brain Tumor Detection
Charissa Poon, Petteri Teikari et al. (2023): "A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging", Scientific Data 10, 141 doi: 10.1038/s41597-023-02048-8
Learning spatially varying regularization for medical image registration
Tensorflow version of DFNN for brain tumor recognition
Repository for our deep multi-frequency fusion paper in DOT imaging.
Cell localization and counting: 1) Exponential Distance Transform Maps for Cell Localization; 2) Multi-scale Hypergraph-based Feature Alignment Network for Cell Localization; 3) Lite-UNet: A lightweight and efficient network for cell localization
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