Swapnil Rane

According to our database, Swapnil Rane authored at least 12 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training.

Abhijeet Patil, Harsh Diwakar, Jay Sawant, Nikhil Cherian Kurian, Subhash Yadav, Swapnil Rane, Tripti Bameta, Amit Sethi

CoRR, 2024

Combining Datasets with Different Label Sets for Improved Nucleus Segmentation and Classification.

Amruta Parulekar, Utkarsh Kanwat, Ravi Kant Gupta, Medha Chippa, Thomas Jacob, Tripti Bameta, Swapnil Rane, Amit Sethi

Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, 2024

2023

EGFR Mutation Prediction of Lung Biopsy Images Using Deep Learning.

Ravi Kant Gupta, Shivani Nandgaonkar, Nikhil Cherian Kurian, Tripti Bameta, Subhash Yadav, Rajiv Kumar Kaushal, Swapnil Rane, Amit Sethi

Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies, 2023

2022

Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge".

Ruchika Verma, Neeraj Kumar, Abhijeet Patil, Nikhil Cherian Kurian, Swapnil Rane, Amit Sethi

IEEE Trans. Medical Imaging, 2022

Deep Multi-Scale U-Net Architecture and Noise-Robust Training Strategies for Histopathological Image Segmentation.

Nikhil Cherian Kurian, Amit Lohan, Gregory Verghese, Nimish Dharamshi, Swati Meena, Mengyuan Li, Fangfang Liu, Cheryl Gillet, Swapnil Rane, Anita Grigoriadis, Amit Sethi

CoRR, 2022

Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation.

Nikhil Cherian Kurian, Amit Lehan, Gregory Verghese, Nimish Dharamshi, Swati Meena, Mengyuan Li, Fangfang Liu, Cheryl Gillet, Swapnil Rane, Anita Grigoriadis, Amit Sethi

Proceedings of the 22nd IEEE International Conference on Bioinformatics and Bioengineering, 2022

2021

A 2021 update on cancer image analytics with deep learning.

Nikhil Cherian Kurian, Amit Sethi, Anil Reddy Konduru, Abhishek Mahajan, Swapnil Ulhas Rane

WIREs Data Mining Knowl. Discov., 2021

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge.

Ruchika Verma, Neeraj Kumar, Abhijeet Patil, Nikhil Cherian Kurian, Swapnil Rane, Simon Graham, Quoc Dang Vu, Mieke Zwager, Shan-E-Ahmed Raza, Nasir M. Rajpoot, Xiyi Wu, Huai Chen, Yijie Huang, Lisheng Wang, Hyun Jung, G. Thomas Brown, Yanling Liu, Shuolin Liu, Seyed Alireza Fatemi Jahromi, Ali Asghar Khani, Ehsan Montahaei, Mahdieh Soleymani Baghshah, Hamid Behroozi, Pavel Semkin, Alexandr Rassadin, Prasad Dutande, Romil Lodaya, Ujjwal Baid, Bhakti Baheti, Sanjay N. Talbar, Amirreza Mahbod, Rupert Ecker, Isabella Ellinger, Zhipeng Luo, Bin Dong, Zhengyu Xu, Yuehan Yao, Shuai Lv, Ming Feng, Kele Xu, Hasib Zunair, Abdessamad Ben Hamza, Steven M. Smiley, Tang-Kai Yin, Qi-Rui Fang, Shikhar Srivastava, Dwarikanath Mahapatra, Lubomira Trnavska, Hanyun Zhang, Priya Lakshmi Narayanan, Justin Law, Yinyin Yuan, Abhiroop Tejomay, Aditya Mitkari, Dinesh Koka, Vikas Ramachandra, Lata Kini, Amit Sethi

IEEE Trans. Medical Imaging, 2021

Robust Classification of Histology Images Exploiting Adversarial Auto Encoders.

Nikhil Cherian Kurian, Gurparkash Singh, Poorvi Hebbar, Shreekanya Kodate, Swapnil Rane, Amit Sethi

Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

2020

A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas.

Ujjwal Baid, Sanjay N. Talbar, Swapnil Rane, Sudeep Gupta, Meenakshi H. Thakur, Aliasgar Moiyadi, Nilesh Sable, Mayuresh Akolkar, Abhishek Mahajan

Frontiers Comput. Neurosci., 2020

Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning.

Ujjwal Baid, Swapnil Rane, Sanjay N. Talbar, Sudeep Gupta, Meenakshi H. Thakur, Aliasgar Moiyadi, Abhishek Mahajan

Frontiers Comput. Neurosci., 2020

2018

Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas.

Ujjwal Baid, Sanjay N. Talbar, Swapnil Rane, Sudeep Gupta, Meenakshi H. Thakur, Aliasgar Moiyadi, Siddhesh Thakur, Abhishek Mahajan

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018