Fast and accurate optimisation for registration with little learningconvexadam

Related tags

Deep Learning convexAdam
Overview

convexAdam

Learn2Reg 2021 Submission

Fast and accurate optimisation for registration with little learning

Slide1 Slide2 Slide3

Excellent results on Learn2Reg 2021 challenge

  • for multimodal CT/MR registration (Task1)
  • intra-patient lung CT alignment (Task2)
  • and inter-patient whole brain MRI deformations (Task3) Challenge Website

Slide4

Results

You might also like...
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022
A little software to generate and save Julia or Mandelbrot's Fractals.
A little software to generate and save Julia or Mandelbrot's Fractals.

Julia-Mandelbrot-s-Fractals A little software to generate and save Julia or Mandelbrot's Fractals. Dependencies : Python 3.7 or more. (Also possible t

Olivier 0 Jul 9, 2022
Much faster than SORT(Simple Online and Realtime Tracking), a little worse than SORT

QSORT QSORT(Quick + Simple Online and Realtime Tracking) is a simple online and realtime tracking algorithm for 2D multiple object tracking in video s

Yonghye Kwon 8 Jul 27, 2022
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible

pyrelational is a python active learning library developed by Relation Therapeutics for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active learning strategies.

Relation Therapeutics 95 Dec 27, 2022
Framework for joint representation learning, evaluation through multimodal registration and comparison with image translation based approaches
Framework for joint representation learning, evaluation through multimodal registration and comparison with image translation based approaches

CoMIR: Contrastive Multimodal Image Representation for Registration Framework 🖼 Registration of images in different modalities with Deep Learning 🤖

Methods for Image Data Analysis - MIDA 55 Dec 9, 2022
An algorithm that handles large-scale aerial photo co-registration, based on SURF, RANSAC and PyTorch autograd.
An algorithm that handles large-scale aerial photo co-registration, based on SURF, RANSAC and PyTorch autograd.

An algorithm that handles large-scale aerial photo co-registration, based on SURF, RANSAC and PyTorch autograd.

Luna Yue Huang 41 Oct 29, 2022
MVP Benchmark for Multi-View Partial Point Cloud Completion and Registration
MVP Benchmark for Multi-View Partial Point Cloud Completion and Registration

MVP Benchmark: Multi-View Partial Point Clouds for Completion and Registration [NEWS] 2021-07-12 [NEW 🎉 ] The submission on Codalab starts! 2021-07-1

PL 93 Dec 21, 2022
🦕 NanoSaur is a little tracked robot ROS2 enabled, made for an NVIDIA Jetson Nano

🦕 nanosaur NanoSaur is a little tracked robot ROS2 enabled, made for an NVIDIA Jetson Nano Website: nanosaur.ai Do you need an help? Discord For tech

NanoSaur 162 Dec 9, 2022
Rayvens makes it possible for data scientists to access hundreds of data services within Ray with little effort.
Rayvens makes it possible for data scientists to access hundreds of data services within Ray with little effort.

Rayvens augments Ray with events. With Rayvens, Ray applications can subscribe to event streams, process and produce events. Rayvens leverages Apache

CodeFlare 32 Dec 25, 2022
Comments
  • How to get the cases.csv

    How to get the cases.csv

    Hi, I tried to run this code for task 1, but I don't have cases.csv (line 45: df = pd.read_csv('/data_supergrover1/hansen/temp/reg/data/abdomen/mr_ct/cases.csv'))

    Can you please provide the corresponding script to generate cases.csv?

    Thanks in advance.

    opened by Jesse233 0
  • How do you use the nnUnet in your code and confuse about your architecture?

    How do you use the nnUnet in your code and confuse about your architecture?

    I saw in your paper at the feature extractor part is MIND and/or nnUnet. The MIND or nnUnet, are they model, right?

    1. The MIND , did you use at the lines 232 and 233, right? So now, I am trying to do the nnUnet model, will I replace losses.MINDSSC by nnUnet, right? Are my mind right?, because I installed this model by pip install nnunet (https://github.com/MIC-DKFZ/nnUNet) but do know import? image

    In short, I am wondering where is your model in your code?. Because your method training time is extremely fast and do not have checkpoints. Normorly, I know orthers method training time is about 20 hours or more.

    Thank you @mattiaspaul.

    opened by tphankr 0
  • AttributeError: module 'pytorch_metric_learning.losses' has no attribute 'MINDSSC'

    AttributeError: module 'pytorch_metric_learning.losses' has no attribute 'MINDSSC'

    Thank you for your code. Please me ask about which "pytorch_metric_learning.losses" version did you use or how do we solution this problem? Because I faced the error when type " python l2r_2021_convexAdam_task2_docker.py " image

    image

    I also used:

    • pip install pytorch_metric_learning==1.0.0 or 0.9.99 or 0.9.98 but all had the above error.

    Thank you.

    opened by tphankr 2
Owner
null
GitHub
Bayesian optimisation library developped by Huawei Noah's Ark Library

Bayesian Optimisation Research This directory contains official implementations for Bayesian optimisation works developped by Huawei R&D, Noah's Ark L

HUAWEI Noah's Ark Lab 395 Dec 30, 2022
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

AutoTrader AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading systems. From sim

Kieran Mackle 485 Jan 9, 2023
GeoTransformer - Geometric Transformer for Fast and Robust Point Cloud Registration

Geometric Transformer for Fast and Robust Point Cloud Registration PyTorch imple

Zheng Qin 220 Jan 5, 2023
Cascading Feature Extraction for Fast Point Cloud Registration (BMVC 2021)

Cascading Feature Extraction for Fast Point Cloud Registration This repository contains the source code for the paper [Arxive link comming soon]. Meth

null 7 May 26, 2022
A lightweight deep network for fast and accurate optical flow estimation.

FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation The official PyTorch implementation of FastFlowNet (ICRA 2021). Authors: Lingtong

Tone 161 Jan 3, 2023
A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

One-Stage Visual Grounding ***** New: Our recent work on One-stage VG is available at ReSC.***** A Fast and Accurate One-Stage Approach to Visual Grou

Zhengyuan Yang 118 Dec 5, 2022
Code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection"

CTDNet The PyTorch code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection" Requirements Python 3.6

CVTEAM 28 Oct 20, 2022
Realtime segmentation with ENet, the fast and accurate segmentation net.

Enet This is a realtime segmentation net with almost 22 fps on GTX1080 ti, and the model size is very small with only 28M. This repo contains the infe

JinTian 14 Aug 30, 2022
Receptive Field Block Net for Accurate and Fast Object Detection, ECCV 2018

Receptive Field Block Net for Accurate and Fast Object Detection By Songtao Liu, Di Huang, Yunhong Wang Updatas (2021/07/23): YOLOX is here!, stronger

Liu Songtao 1.4k Dec 21, 2022
This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes Introduction This is the unofficial code of Deep Dual-re

chenjun 113 Dec 23, 2022