GingerSpacetail's repositories
OpenCRISPR
AI-generated gene editing systems
itkwidgets
An elegant Python interface for visualization on the web platform to interactively generate insights into multidimensional images, point sets, and geometry.
stable-diffusion-webui
Stable Diffusion web UI
python_for_microscopists
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
trackpy
Python particle tracking toolkit
GingerSpacetail
Config files for my GitHub profile.
GPT-actions
Fabrika Actions for Assistants powered by OpenAI
workout_assistant1.0
llama.cpp with BakLLaVA model compares your body pose with the reference and provides natural language feedback
SimpliPyTEM-sandbox
Package to make analysis of transmission electron microscopy images simple.
Chest_X_Ray_Medical_Diagnosis_with_Deep_Learning
A deep learning classifier model for a dataset annotated by consensus among four different radiologists for 5 of our 14 pathologies
Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks
Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.
Deep-classifier-skis-cancer-images-into-Melanoma-and-Nevi-classes-Transfer-learning-GE-lab
Aim is to classify skis cancer images into 2 classes (Melonoma and Nevi) by using the concept of transfer learning (feature extraction from a pre-trained model + Multi-Layer Perceptron)
Medical-biomarkers-mining-Feature-Extraction-GE-lab
Extracting first order statistics and textural features on tumour deliniated PET-CT images for the survival status prediction
Classification-benign-or-malignant-pulmonary-nodules-Random-Forest-GE-lab
Classification problem benign or malignant pulmonary nodules on CT images solved with Random Forest and k-fold cross validated
Binary-classification-ovarian-cancer-or-healthy-subject-SVM-GE-lab
Support Vector Machines (SVM) review as a powerful class of supervised classification and clinical Proteomics example
Labelling-rules-influence-on-Multinomial-Naive-Bayes-classifier-SPAM-noSPAM
Explore how different strategies affect the performance of a machine learning model by simulating the process of having different labelers label the data
Multi-task-models-with-Keras-handwritten-digit-and-color-recognition
A model architecture with two outputs given one input. Two tasks: classifying handwritten digits into 10 classes (0 to 9) and binary classification btw two predominant color channels (red & green)
Data-centric-approach-adressing-class-imbalance-and-overfitting-in-Convolutional-Neural-Network-CNN
The paradigm behind Deep Learning is now facing a shift from model-centric to data-centric. Data intricacies may affect the outcome of a model. Data changes are applied without addressing the model. A simple Convolutional Neural Network (CNN) is being used to show how data augmentation can help with the following common problems: class imbalance and overfitting.
Binary-classification-malignant-or-benign-breast-cancer-KNN-GE-lab
A two class classification problem. The dataset contains 569 subjects from each 30 features were extracted and labeled as 1 or 0 to present the malignant or benign breast cancer
github-slideshow
A robot powered training repository :robot: