Matthews Correlation Coefficient Loss implementation for image segmentation.

Yuval and nosound models and write-up for Kaggle's competition "SIIM-ISIC Melanoma Classification"

Skin Lesion Detector using HAM10000 dataset with Chainer / ChainerCV

Fully automatic skin lesion segmentation using the Berkeley wavelet transform and UNet algorithm.

Testing the consistency of binary classification performance scores reported in papers

ISIC 2018 - Skin Lesion Classification for Melanoma Detection

Fully supervised binary classification of skin lesions from dermatoscopic images using multi-color space moments/texture features and Support Vector Machines/Random Forests.

Skin lesion classification, using Keras and the ISIC 2020 dataset

Skin lesion segmentation using a new ensemble deep network model and an incremental learning approach

Skin lesion image analysis that draws on meta-learning to improve performance in the low data and imbalanced data regimes.

[CIBM'2021] Knowledge Distillation approach towards Melanoma Detection

Machine Learning 2 Course Project at RKMVERI, 2021. Published at The Imaging Science Journal (2023), Paper: https://www.tandfonline.com/doi/full/10.1080/13682199.2023.2174657

Skin caner detection application with convolutional neural network utilizing skin lesion images

PyTorch model that uses triplet loss to find the image with most similar skin condition

RECOD Titans @ SIIM-ISIC Melanoma Classification

Analysis of the dermoscopic image processing pipeline toward optimally segmenting skin lesion regions and classifying lesion types using adversarial and generative deep learning.