Pinned Repositories
boyden.github.io
Personal Academic Homepage of Bao Li
mmdetection
OpenMMLab Detection Toolbox and Benchmark
NuHTC
NuHTC: A Hybrid Task Cascade for Nuclei Instance Segmentation and Classification
OpenCV2-Python-Tutorials
This repo contains tutorials on OpenCV-Python library using new cv2 interface
PointTransformerFL
A Point Transformer with Federated Learning for HER2 Status Prediction
Pretreatment-Prediction-to-NAC-DLPM
Deep Learning Pathological Model for predicting pCR to NAC in breast cancer using WSIs
pyradiomics
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
SlicerBreast_DCEMRI_FTV
Repository for the 3D Slicer extension Breast DCE-MRI FTV
USTC-Course
:heart:**科学技术大学课程资源
USTC-CS-Courses-Resource
:heart:**科学技术大学计算机学院课程资源(https://mbinary.xyz/ustc-cs/)
boyden's Repositories
boyden/NuHTC
NuHTC: A Hybrid Task Cascade for Nuclei Instance Segmentation and Classification
boyden/PointTransformerFL
A Point Transformer with Federated Learning for HER2 Status Prediction
boyden/boyden.github.io
Personal Academic Homepage of Bao Li
boyden/mmdetection
OpenMMLab Detection Toolbox and Benchmark
boyden/OpenCV2-Python-Tutorials
This repo contains tutorials on OpenCV-Python library using new cv2 interface
boyden/Pretreatment-Prediction-to-NAC-DLPM
Deep Learning Pathological Model for predicting pCR to NAC in breast cancer using WSIs
boyden/pyradiomics
Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
boyden/SlicerBreast_DCEMRI_FTV
Repository for the 3D Slicer extension Breast DCE-MRI FTV
boyden/USTC-Course
:heart:**科学技术大学课程资源
boyden/USTC-CS-Courses-Resource
:heart:**科学技术大学计算机学院课程资源(https://mbinary.xyz/ustc-cs/)
boyden/visualkeras
Visualkeras is a Python package to help visualize Keras (either standalone or included in TensorFlow) neural network architectures. It allows easy styling to fit most needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.