Kaggle: Cell Instance Segmentation

Last update: Aug 12, 2022

Overview

Kaggle: Cell Instance Segmentation

The goal of this challenge is to detect cells in microscope images.

with simple view on how many cels have been annotated per image:

Experimentation

install this tooling

A simple way how to use this basic functions:

! pip install https://github.com/Borda/kaggle_cell-inst-segm/archive/refs/heads/main.zip

run notebooks in Kaggle

local notebooks

some results

Training progress with EfficientNet3D with training for 10 epochs > over 96% validation accuracy:

Owner

Jirka Borovec

Computer Vision & Machine Learning & Bio-Medical Imaging & DeepLearn

GitHub Repository

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Kaggle: Cell Instance Segmentation

Related tags

Overview

Kaggle: Cell Instance Segmentation

Experimentation

install this tooling

run notebooks in Kaggle

local notebooks

some results

Owner

Jirka Borovec

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

Implementation of Ag-Grid component for Streamlit

Tianshou - An elegant PyTorch deep reinforcement learning library.

This repo includes our code for evaluating and improving transferability in domain generalization (NeurIPS 2021)

Single cell current best practices tutorial case study for the paper:Luecken and Theis, "Current best practices in single-cell RNA-seq analysis: a tutorial"

Unsupervised Foreground Extraction via Deep Region Competition

Code for ACL2021 paper Consistency Regularization for Cross-Lingual Fine-Tuning.

codes for Image Inpainting with External-internal Learning and Monochromic Bottleneck

Code for "Continuous-Time Meta-Learning with Forward Mode Differentiation" (ICLR 2022)

Codebase for the Summary Loop paper at ACL2020

Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch

Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

PyTorch implementation of the paper Ultra Fast Structure-aware Deep Lane Detection

Lucid library adapted for PyTorch

Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras

Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population