ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection

I participated in this Kaggle competition to create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. The data for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset. I got 65th position from 1157 and is in top 6\%.

I also contributed to write a paper “Semi-Supervised Learning for Cancer Detection of Lymph Node Metastase” that has been accepted in workshop Towards Causal, Explainable and Universal Medical Visual Diagnosis, CVPR 2019

[![] (https://s1155026040.github.io/mvd-2019-cvpr-workshop) ] (https://s1155026040.github.io/mvd-2019-cvpr-workshop)

Results

Model Accuracy, Loss, and Training Time

Model Inference