Dissertation and Thesis
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Item Telluric Correction of M-dwarf Stars using Machine Learning(Indian Statistical Institute, 2026-06-17) Rana, SayakThe study of M-dwarf stars is of prime scientific interest to us because of their closer habitable zones and the favorable conditions they offer for exoplanet detection. However, telluric contamination of the ground-based spectra results in sharp absorption lines, which makes their study cumbersome. Removing this contamination is necessary for estimating key stellar parameters. The central contribution is a one-dimensional Convolutional Neural Network (CNN) that retrieves the four atmospheric parameters governing telluric absorption: pressure, temperature, humidity, and airmass. These predicted parameters are passed to Telfit which produces an estimated telluric spectrum. The observed spectrum is then divided by this estimated telluric spectrum to obtain the telluric corrected spectrum. As this network is trained exclusively on synthetic spectra, a domain gap exists at inference. Two domain-adaptation strategies are evaluated: a CycleGAN following the Cycle-StarNet framework for explicit synthetic-to-real translation and a Domain-Adversarial Autoencoder (DAAE) that learns domain-invariant spectral representations. The Domain-Adversarial Autoencoder (DAAE) achieves the lowest loss against a telluric corrected CARMENES reference spectrum outperforming all other model variants. The CycleGAN fails due to discriminator collapse under severe class imbalance between the number of real and synthetic spectra.Item Exploring Resource-Efficient Deep Learning for Medical Image Segmentation(2026-05-19) Dutta, PallabiAutomated medical image segmentation improves diagnostic accuracy by au tomating the precise delineation of target anatomical structures in the input images. Artificial Intelligence (AI), and specifically, Deep Learning (DL), has emerged as a state-of-the-art approach for this task. However, the significant computational demands of DL approaches often hinders their deployment. Ad vanced models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), require substantial processing power and a large memory footprint, limiting their use in resource-constrained settings. This thesis aims to address this challenge by developing a series of novel, resource-efficient DL models that achieve high segmentation accuracy with reduced computational costs. The research follows a logical progression of architectural novelty. First, global context-aware attention frameworks, FuDSA-Net and VoCANet, are in troduced by leveraging multi-scalar features and global-context aware attention for efficient 2D/3D segmentation. The spatial and spectral domains are then integrated using a novel hybrid CNN-ViT framework WaveCoformer for learn ing robust representation of the target structure. The developed model achieves high segmentation accuracy with a lower parameter count. Subsequently, the research investigates a computationally efficient alternative to ViTs for segmen tation, called Vision-xLSTM, by developing the U-VixLSTM model. This is extended to the Rot-UViL architecture, capable of modeling cross-dimensional dependencies in volumetric inputs with its novel rotational attention. Finally, the thesis presents a prompt-driven pruning framework for ViT-based segmenta tion models, called PrATo, which dynamically prunes irrelevant ViT tokens with a parameter-free prompt-driven scoring mechanism. The framework achieves ∼ 35−55% reduction of processed tokens. The frameworks developed in this thesis are validated across multiple publicly available datasets; demonstrating their high segmentation accuracy along with computational efficiency.Item KNEE MRI DIAGNOSIS USING SELFSUPERVISED LEARNING(Indian Statistical Institute, Kolkata, 2022-07) Ray, SayanAbstract