htic-medical-imaging Goto Github PK

adn

ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

ahrq-cds-connect-authoring-tool

The CDS Authoring Tool is part of the CDS Connect project https://cds.ahrq.gov/, sponsored by the Agency for Healthcare Research and Quality (AHRQ), and developed under contract with AHRQ by MITRE's CAMH FFRDC.

conda-docker-images

Repository of Docker images created by Continuum Analytics

count-ception_mbm

cudalibrarysamples

CUDA Library Samples - for nvjpeg2000

deep-learning-on-graphs

hbp_aria-vesselthickness

Retinal vessel analysis software for MATLAB

hbp_cellfinder-core

Standalone cellfinder cell detection algorithm

hbp_cellpose

a generalist algorithm for cellular segmentation with human-in-the-loop capabilities

hbp_centermask_src

[CVPR 2020] CenterMask : Real-time Anchor-Free Instance Segmentation

hbp_clearmap2

ClearMap 2.0 with WobblyStitcher, TubeMap and CellMap

hbp_coda

Nat Meth 2022 CODA quantitative 3D reconstruction of large tissues at cellular resolution

hbp_cpn-celldetection

contour proposal networks Cell Detection with PyTorch.

hbp_dali_expts

hbp_dataview

hbp_fiji-auto_local_threshold

fiji auto local threshold algorithms

hbp_hover_net

Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.

hbp_monailabel_workspace

hbp_msau-net

multiscale split attention unet for nuclei segmentation

hbp_stardist

StarDist - Object Detection with Star-convex Shapes

ilastik

ilastik-shell, applets, and workflows to string them together.

nissl-residual_attention_unet

nissl-yolov5

onnx-tensorrt

ONNX-TensorRT: TensorRT backend for ONNX

problemsets

Problem sets from practiceprobs.com

ptychopy

High performance ptychography reconstruction python package running on GPU

pyhealth

A Python Library for Health Predictive Models

raunet

RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments (ICONIP19)

trt_hovernet

unsupervised_anomaly_detection_brain_mri

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study