Published in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
This paper introduces robust sample specific loss functions for histology image analysis. Read more
Published in 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021
This paper introduces novel self-supervision based color-normalisation schemes for H&E images. Read more
Published in Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2021
A review on the latest updates on cancer image analytics with deep learning. Read more
Published in Transaction of Medical Imaging, IEEE, 2021
In this paper, we present the key findings of MoNuSAC challenge at ISBI 2020 Read more
Published in AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging;, 2020
In this work, we propose to classify cancers using graph convolutional networks (GCNs) by modeling a tissue section as a multi-attributed spatial graph of its constituent cells. Read more
Published in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
This paper presents a deep learning pipeline for assessing bathymetry maps from satellite derived aerial images . Read more
Published in Journal of Pathology Informatics, 2019
This paper provides a proof-of-concept for detection of genetic mutation in breast cancer patients from H&E images instead of using costly IHC images. Read more
Published in ESMO MAP, 2019
This paper provides a CNN based detection of pathological features in lymph nodes to analyse early signs of breast cancer Read more
Published in IEEE TENCON, 2019
This paper proposes a preprocessing block by adding location specific biases and residual filters that address the generalisation issues between histology datasets. Read more
Published in Applied Acoustics, 2016
In this paper, we propose a novel information theoretic loss functions for adaptive signal processing under alpha stable distributions. Read more
Published in 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2016
This paper reduces tthe computational burden for calculating S-transorm using the ideas of sparse FFT. Read more