Publications by authors named "Raktim Kumar Mondol"
Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients.
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- Gene expression can improve breast cancer subtype classification and treatment predictions compared to traditional methods, but current molecular profiling is limited and costly.
- Deep learning can quickly and affordably extract patterns from digital pathology images to predict gene expression, using a new method called hist2RNA.
- This approach accurately predicts gene expression type and prognosis by analyzing H&E-stained images, demonstrating better efficiency and performance than existing models while being less resource-intensive.
AFExNet: An Adversarial Autoencoder for Differentiating Breast Cancer Sub-Types and Extracting Biologically Relevant Genes.
IEEE/ACM Trans Comput Biol Bioinform
August 2022
Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g.
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