Publications by authors named "Debanjan Konar"
3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images.
IEEE Trans Neural Netw Learn Syst
August 2024
This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz.
View Article and Find Full Text PDFChildlessness or infertility among couples has become a global health concern. Due to the rise in infertility, couples are looking for medical supports to attain reproduction. This paper deals with diagnosing infertility among men and the major factor in diagnosing infertility among men is the Sperm Morphology Analysis (SMA).
View Article and Find Full Text PDFQutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation.
IEEE Trans Neural Netw Learn Syst
November 2022
Article Synopsis
- Classical self-supervised networks struggle with convergence and segmentation accuracy, prompting the development of a new model called the quantum fully self-supervised neural network (QFS-Net) for brain MR image segmentation.
- The QFS-Net utilizes a three-level qutrit quantum information system and a unique layered structure interconnected by Hadamard gates, allowing it to perform faster, unsupervised learning.
- Tested on the Cancer Imaging Archive dataset, QFS-Net showed superior performance in detecting tumors compared to existing models like U-Net and URes-Net, requiring less human involvement and computational power, while also displaying strong results on natural gray-scale images.