Introduction
Published:
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
Results
| Confusion Matrix (Normalized) | Confusion Matrix |
|---|---|
 | |
| Inference |
|---|
 |
Results Transfer Learning
Citation
- Jaiswal, Amit Kumar, Ivan Panshin, Dimitrij Shulkin, Nagender Aneja, and Samuel Abramov. “Semi-supervised learning for cancer detection of lymph node metastases.” arXiv preprint arXiv:1906.09587 (2019).