Unsupervised clustering of high content screen samples

Overview

Microscopium

Unsupervised clustering and dataset exploration for high content screens.

Build Status Coverage Status Gitter

See microscopium in action

microscopium_bbbc021

Public dataset BBBC021 from the Broad Bioimage Benchmark Collection with t-SNE image embedding.

For developers

We encourage pull requests - please get in touch if you think you might like to contribute.

License

This project uses the 3-clause BSD license. See LICENSE.txt.

Development installation

First, clone this repository, and change into its directory:

git clone https://github.com/microscopium/microscopium.git
cd microscopium

Then, install the dependencies via one of the below methods

conda, new environment (recommended)

conda env create -f environment.yml
conda activate mic

conda, existing environment

# conda activate <env-name>
conda install -f environment.yml

pip

pip install -r requirements.txt

Finally, install microscopium, optionally as an editable package:

pip install [-e] .

Serving the web app

Supported browsers are Chrome and Firefox. However we have observed that performance is much better on Chrome. (Unfortunately, we do not currently support Safari or Internet Explorer.)

Your data needs to have the following format:

  • a collection of image files (can be in a directory, or in a nested directory structure)
  • a .csv file containing, at a minimum, the x/y coordinates of each image, and the path to the image in the directory. The path should be relative to the location of the .csv file.
  • a .yaml file containing settings. At a minimum, it should contain an embeddings field with maps from <embedding name> to column names for x and y, as well as image-column containing the name of the column containing the path to each image. If you don't want to specify the settings file path, place settings.yaml next to the .csv file. Microscopium will look here by default.

For example data, see:

  • tests/testdata/images/*.png
  • tests/testdata/images/data.csv
  • tests/testdata/images/settings.yaml

To run the web app locally, try:

python -m microscopium.serve tests/testdata/images/data.csv -c tests/testdata/images/settings.yaml

You should then be able to see the app in your web browser at: http://localhost:5000

You can specify a port number with -P.

python -m microscopium.serve tests/testdata/images/data.csv -P 5001

This specifies the port number as 5001, and the app will run locally at: http://localhost:5001/

For more information, run python -m microscopium.serve --help

DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

Differentiable Model Compression via Pseudo Quantization Noise DiffQ performs differentiable quantization using pseudo quantization noise. It can auto

Facebook Research 145 Dec 30, 2022
Code for Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks Under construction. Description Code for Phase diagram of S

Rodrigo Veiga 3 Nov 24, 2022
基于PaddleClas实现垃圾分类,并转换为inference格式用PaddleHub服务端部署

百度网盘链接及提取码: 链接:https://pan.baidu.com/s/1HKpgakNx1hNlOuZJuW6T1w 提取码:wylx 一个垃圾分类项目带你玩转飞桨多个产品(1) 基于PaddleClas实现垃圾分类,导出inference模型并利用PaddleHub Serving进行服务

thomas-yanxin 22 Jul 12, 2022
A (PyTorch) imbalanced dataset sampler for oversampling low frequent classes and undersampling high frequent ones.

Imbalanced Dataset Sampler Introduction In many machine learning applications, we often come across datasets where some types of data may be seen more

Ming 2k Jan 08, 2023
ANN model for prediction a spatio-temporal distribution of supercooled liquid in mixed-phase clouds using Doppler cloud radar spectra.

VOODOO Revealing supercooled liquid beyond lidar attenuation Explore the docs » Report Bug · Request Feature Table of Contents About The Project Built

remsens-lim 2 Apr 28, 2022
A general framework for deep learning experiments under PyTorch based on pytorch-lightning

torchx Torchx is a general framework for deep learning experiments under PyTorch based on pytorch-lightning. TODO list gan-like training wrapper text

Yingtian Liu 6 Mar 17, 2022
Implementation of "RaScaNet: Learning Tiny Models by Raster-Scanning Image" from CVPR 2021.

RaScaNet: Learning Tiny Models by Raster-Scanning Images Deploying deep convolutional neural networks on ultra-low power systems is challenging, becau

SAIT (Samsung Advanced Institute of Technology) 5 Dec 26, 2022
Repository of our paper 'Refer-it-in-RGBD' in CVPR 2021

Refer-it-in-RGBD This is the repository of our paper 'Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images' in CVPR 2021 Pape

Haolin Liu 34 Nov 07, 2022
RSNA Intracranial Hemorrhage Detection with python

RSNA Intracranial Hemorrhage Detection This is the source code for the first place solution to the RSNA2019 Intracranial Hemorrhage Detection Challeng

24 Nov 30, 2022
Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking."

Expert-Linking Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking." This is

BoChen 12 Jan 01, 2023
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, CVPR 2021

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning By Zhenda Xie*, Yutong Lin*, Zheng Zhang, Yue Ca

Zhenda Xie 293 Dec 20, 2022
Code Release for ICCV 2021 (oral), "AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds"

AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds (ICCV 2021 oral) **Project Page | Arxiv ** Runsong Zhu¹, Yuan Liu², Zhen Dong¹, Te

40 Dec 30, 2022
[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Robot Action Primitives (RAPS) This repository is the official implementation of Accelerating Robotic Reinforcement Learning via Parameterized Action

Murtaza Dalal 55 Dec 27, 2022
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data This is the official PyTorch implementation of the SeCo paper: @articl

ElementAI 101 Dec 12, 2022
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

DeCLIP Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm. Our paper is available in arxiv Updates ** Ou

Sense-GVT 470 Dec 30, 2022
Multi-tool reverse engineering collaboration solution.

CollaRE v0.3 Intorduction CollareRE is a tool for collaborative reverse engineering that aims to allow teams that do need to use more then one tool du

105 Nov 27, 2022
A robotic arm that mimics hand movement through MediaPipe tracking.

La-Z-Arm A robotic arm that mimics hand movement through MediaPipe tracking. Hardware NVidia Jetson Nano Sparkfun Pi Servo Shield Micro Servos Webcam

Alfred 1 Jun 05, 2022
QTool: A Low-bit Quantization Toolbox for Deep Neural Networks in Computer Vision

This project provides abundant choices of quantization strategies (such as the quantization algorithms, training schedules and empirical tricks) for quantizing the deep neural networks into low-bit c

Monash Green AI Lab 51 Dec 10, 2022
Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees" Installa

0 Oct 13, 2021
"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

NAS-Bench-301 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The

AutoML-Freiburg-Hannover 57 Nov 30, 2022