Analysis of the dermoscopic image processing pipeline toward optimally segmenting skin lesion regions and classifying lesion types using adversarial and generative deep learning.
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Updated
Jun 4, 2024 - Jupyter Notebook
Analysis of the dermoscopic image processing pipeline toward optimally segmenting skin lesion regions and classifying lesion types using adversarial and generative deep learning.
Source code for the paper: "Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?"
My machine learning notebooks. Feel free to use for your purposes.
Skin Lesion Classifier using the ISIC 2018 Task 3 Dataset.
U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation
ISIC Challenge - Lesion Segmentation task solved using the U-Net model building from scratch
The aim of this study is to develop a deep learning model using CNNs for accurate skin cancer diagnosis from the ISIC-2019 dataset and to optimize hyperparameters using differential evolution algorithms.
Developing a CNN-based model to diagnose skin cancer using the ISIC-2019 dataset.
ISIC2019 skin lesion classification (binary & multi-class) as well as segmentation pipelines using VGG16_BN and visual attention blocks. The project features improving the results found in the literature by implementing an ensemble architecture. This project was developed for "Computer Aided Diagnosis - CAD" course for MAIA masters program.
Machine Learning Model to Skin Tumor Analysis and Classification.
The official command line tool for interacting with the ISIC Archive.
Skin Lesion Classifier: a skin lesion analysis towards melanoma detection.
The souce code of MICCAI'23 paper: Combat Long-tails in Medical Classification with Relation-aware Consistency and Virtual Features Compensation
RECOD Titans @ SIIM-ISIC Melanoma Classification
Source code and experiments for the paper: "Dark Corner on Skin Lesion Image Dataset: Does it matter?"
ISIC Challenge submission platform.
Instructions for the removal of duplicate image files from within individual ISIC datasets and across all ISIC datasets.
Fully automatic skin lesion segmentation using the Berkeley wavelet transform and UNet algorithm.
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