Sep 2023 — Mar 2024

Graph Convolutional Network based Multi-modal Learning Approaches for Breast Cancer Survival Prediction

Developed advanced multi-modal deep learning frameworks to address the critical challenge of accurately predicting breast cancer prognosis and survival outcomes. The first framework, MGCN-CalRF, utilizes multimodal Graph Convolutional Networks to extract modality-specific relational features and integrates them using a calibrated Random Forest classifier for prognosis prediction. The second framework, CAGCL, applies graph contrastive learning to refine feature embeddings and incorporates a cross-modality attention mechanism to model complementary information across heterogeneous data modalities for survival prediction.

IIT Patna Collaborators: Dr. Susmita Palmal, Prof. Somanath Tripathy, Dr. Sriparna Saha
Sep 2023 — Jan 2024

Multi-modal Breast-Cancer Survival Estimation and Multi-modal Skin Lesion classification.

Literature Survey on deep multi-modal architecture for accurately estimating the survival of breast cancer patients. The architecture is aimed to utilize six different modalities from multi-omics, clinical, and histopathology profiles of TCGA-BRCA data. We also worked on multi-modal deep learning-based architectures for skin lesion classification using dermoscopy images and demographic information of the patients.

KMITL Thailand, BITS Pilani, IIT Patna Collaborators: Dr. Sriparna Saha, Prof. Kitsuchart Pasupa, Prof. Snehanshu Saha, Dr. Archana Mathur
Jul 2022 — Jan 2023

Towards Stability and Robustness of AI-based Multi-modal Machine Learning Classifiers for Breast Cancer Prognosis Prediction

Developed some state-of-the-art machine learning classifiers by incorporating additional sources of information from six different modalities which include clinical, genetic, and histopathological tissue images for breast cancer survival prediction. Improving stability and robustness of these classifiers by introducing some deep learning-based feature extraction techniques followed by a proposal of advanced kernel function for the Support Vector Machine classifier

BITS Pilani, IIT Patna Collaborators: Prof. Snehanshu Saha, Dr. Sriparna Saha, Dr. Archana Mathur
Jan 2022 — May 2023

Developing Ensemble techniques for Breast Cancer Survival Prognosis

Proposed variors ensemble architectures for the multi-modal breast cancer surviavl prognosis. "DeSuFEn" is one of them which uses rewarding algorithm to counter the effect of class imbalance and fuzzy ensemble to dynamically allocate the importance of each base classifier towards getting better prediction accuracies. Further, enhancement of fuzzy ensemble with the help of two non-linear functions acting as deviation and support for breast cancer survival prediction.

IIT Patna Collaborators: Dr. Sriparna Saha
Jan 2021 — Dec 2021

AI-based Generative Models to Handle Missing Data in Multi-modal Framework for Breast Cancer Prognosis.

Developed a deep learning-based generative model for handling cases where patients are not having information from all the modalities. The generative model uses the power of attention technique and Generative Adversarial Networks to generate the missing data from the available data. Further developed deep learning-based classifiers using the generated and available data towards breast cancer survival prediction.

IIT Patna Collaborators: Dr. Sriparna Saha
Jul 2018 — Dec 2020

Deep Learning Multi-modal Architectures for Breast Cancer Prognosis

Analysed different modalities of information from gene expression, copy number variation, and clinical details of breast cancer patients towards the prognosis prediction task. Developed various deep learning architectures that incorporate the concept of convolutional neural networks, attention mechanisms, and stacking of machine learning classifiers over deep learning architectures to classify the patients as long-term vs short-term survivors.

IIT Patna Collaborators: Dr. Sriparna Saha