The segmentation model for ECCV 2024 paper StyleCity
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Updated
Nov 9, 2025 - Python
The segmentation model for ECCV 2024 paper StyleCity
Grand Challenge 2017 Multi-Modality Whole Heart Segmentation
[ICPR 2020] PS2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation
End-to-end UNet3D stack for volumetric medical segmentation: training utilities, FastAPI inference API, Streamlit review UI, Docker deployment, and a model card. Upload NIfTI volumes, get masks back, and visualize slices/IoU plots from experiments. Built for reproducible research and production-grade serving
Abdomen 3D Segmentation Using UNETR: Tool for segmenting abdominal organs using the UNETR model. Combines Transformers with CNNs for precise 3D segmentation. Dive into medical imaging! 🏥✨🔍
The code for the paper "Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models" (ICCV'23).
This repository is made for all learners. If you like it, do give it a star.
The official implementation of Lite ENSAM, a lightweight cancer segmentation model for 3D Computed Tomography.
The program is designed for segmentation and creating 3D models of certain organs from DICOM images
Automatic bounding box detection using masks, image cropping, and volume storage
Solution (Top 6) for 3D segmentation of ovarian cancer with peritoneal carcinosis using CT scans.
This GitHub repository was created for research focusing on the development of deep learning-based segmentation models for fetal brain tissue.
SOTA 3D Medical Segmentation framework (UNET++, TransUNET , SwimTransUNET) in TensorFlow. Fully TPU-optimized with a 3-stage OHEM pipeline for the Medical Segmentation Decathlon (MSD).
Stagnant zone segmentation with U-net
Brain Segmentation
Our image analysis software performs segmentation of the cellular areas with cell surface expression of the prostate-specific membrane antigen to improve the precision of therapy and its customization
🧠 Segment brain tumors accurately using a 2D U-Net model in PyTorch with the BraTS 2020 dataset for efficient evaluation and visualization.
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