71 skills found · Page 1 of 3
TissueImageAnalytics / TiatoolboxComputational Pathology Toolbox developed by TIA Centre, University of Warwick.
21Vipin / Medical Image Classification Using Deep LearningTumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.
prabhakarlab / Banksy PyBANKSY: Spatial Clustering Algorithm that Unifies Cell-Typing and Tissue Domain Segmentation. Python package for spatial transcriptomics analysis.
poseidonchan / TAPEDeep learning-based tissue compositions and cell-type-specific gene expression analysis with tissue-adaptive autoencoder (TAPE)
ChristophKirst / ClearMapClearMap is a python toolbox for the analysis and registration of volumetric data from cleared tissues.
AtlasAnalyticsLab / AtlasPatchAtlasPatch: An Efficient and Scalable Tool for Whole Slide Image Preprocessing
StructuralNeurobiologyLab / SyConnToolkit for the generation and analysis of volume eletron microscopy based synaptic connectomes of brain tissue.
ciccalab / SIMPLISIMPLI is a highly configurable pipeline for the analysis of multiplexed imaging data.
AltschulerWu-Lab / MUSEMUSE is a deep learning approach characterizing tissue composition through combined analysis of morphologies and transcriptional states for spatially resolved transcriptomics data.
GuangyuWangLab2021 / ThorCell level integrated analysis of tissue histology and spatial transcriptomics
qqwang-berkeley / JUMA tool for annotation-free differential analysis of tissue-specific pre-mRNA alternative splicing patterns
Mr-Milk / SpatialTisSpatial analysis toolkit for single cell multiplexed tissue data
10XGenomics / Janesick Nature Comms 2023 CompanionCode companion to the publication "High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue"
theislab / Covid Meta AnalysisAnalysis notebooks for the Covid-19 meta analysis that accompanies the Nature Medicine publication "Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics"
huBioinfo / CytoCommunityA spatial omics data analysis tool that enables both unsupervised and supervised discovery of complex tissue cellular neighborhoods from cell phenotypes.
jopo666 / HistoPrepPreprocessing module for large histological images
lawrenson-lab / AtlasEndometriosisA single-cell transcriptomic analysis of endometriosis, endometriomas, eutopic endometrial samples and uninvolved ovary tissues highlights cell populations characteristic of these tissue types. Transcriptional and cellular heterogeneity across tissues suggests novel therapeutic targets and biomarkers for this disease.
cancer-evolution / SPIATSpatial Image Analysis of Tissues
jzxu0622 / MatiMATI (Microstructural Analysis of Tissues by Imaging)
asmagen / RobustSingleCellRobust single cell clustering and comparison of population compositions across tissues and experimental models via similarity analysis.
