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Achievement: QuickdrawAchievement: Pull Sharkx2Achievement: Pair ExtraordinaireAchievement: YOLO

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GingerSpacetail/README.md

👋 Hi, I’m @GingerSpacetail
👀 I’m interested in AI in Healthcare
🌱 I’m a ML professional and a constant learner with competencies in MedTech, Maths and Economics on a mission to create value in the healthcare industry
💞️ I’m happy to collaborate on AI in Healthcare:

  • NLP (from patient records or biopsy reports to molecular design),
  • Computer vision problems in medical imaging (e.g. organs, tissues, target structures delineation),
  • ML for radiology and radiotherapy (e.g. image biomarkers feature extraction, classification problems for malignant /benign tumor, dose delivery and distribution, SPECT/PET/MRI reconstruction algorithms improvement),
  • DL deployment in genomics, radiomics, multi-omics.

📫 How to reach me: telegram @gingerspacetail

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  1. Binary-classification-malignant-or-benign-breast-cancer-KNN-GE-lab Binary-classification-malignant-or-benign-breast-cancer-KNN-GE-lab Public

    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

    Jupyter Notebook

  2. Data-centric-approach-adressing-class-imbalance-and-overfitting-in-Convolutional-Neural-Network-CNN Data-centric-approach-adressing-class-imbalance-and-overfitting-in-Convolutional-Neural-Network-CNN Public

    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…

    Jupyter Notebook

  3. Binary-classification-ovarian-cancer-or-healthy-subject-SVM-GE-lab Binary-classification-ovarian-cancer-or-healthy-subject-SVM-GE-lab Public

    Support Vector Machines (SVM) review as a powerful class of supervised classification and clinical Proteomics example

    Jupyter Notebook

  4. Deep-classifier-skis-cancer-images-into-Melanoma-and-Nevi-classes-Transfer-learning-GE-lab Deep-classifier-skis-cancer-images-into-Melanoma-and-Nevi-classes-Transfer-learning-GE-lab Public

    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)

    Jupyter Notebook

  5. Medical-biomarkers-mining-Feature-Extraction-GE-lab Medical-biomarkers-mining-Feature-Extraction-GE-lab Public

    Extracting first order statistics and textural features on tumour deliniated PET-CT images for the survival status prediction

    Jupyter Notebook 1 1

  6. Chest_X_Ray_Medical_Diagnosis_with_Deep_Learning Chest_X_Ray_Medical_Diagnosis_with_Deep_Learning Public

    A deep learning classifier model for a dataset annotated by consensus among four different radiologists for 5 of our 14 pathologies

    Jupyter Notebook