MARC details
| 000 -LEADER |
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04533nam a22003137a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
th647 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ISI Library, Kolkata |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250916145853.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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250916b |||||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
ISI Library |
| Language of cataloging |
English |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
23rd |
| Classification number |
616.0754 |
| Item number |
G427 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Ghosh, Susmita |
| Relator term |
author |
| 245 10 - TITLE STATEMENT |
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| Title |
Enhancing medical image analysis through deep learning: a comprehensive study on classification, segmentation, and multitask learning/ |
| Statement of responsibility, etc |
Susmita Ghosh |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Kolkata: |
| Name of publisher, distributor, etc |
Indian Statistical Institute, |
| Date of publication, distribution, etc |
2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
ix, 181 pages |
| Dimensions |
charts, ills |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Ph. D.) - Indian Statistical Institute, 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliography |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Foundations of Deep Learning in Medical Imaging: A Prelude -- A Deep Learning Framework Integrating the Spectral and Spatial Features for Image-assisted Medical Diagnostics -- An Improved Vision Transformer Model for Medical Image based Diagnostic Solution -- Multi-scale Morphology-aided Deep Medical Image Segmentation -- MA-DTNet: Multi-task Learning with Morphological Attention for Medical Image Analysis -- Conclusion |
| 508 ## - CREATION/PRODUCTION CREDITS NOTE |
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| Creation/production credits note |
Guided by Prof. Swagatam Das |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Medical image analysis has become indispensable for accurate diagnosis and treatment planning. However, despite advances in deep learning, several critical challenges persist, ranging from more efficient models to the integration of multiple tasks within a unified framework. This thesis addresses these challenges by proposing innovative deep learn- ing architectures that enhance medical image classification, segmentation, and multitask learning. At the heart of this research is the goal of developing models that deliver high performance and tackle the nuanced complexities of medical data. Existing clas- sification models often overlook valuable information hidden in the spectral domain of images. I address this by integrating spatial and spectral features, demonstrating their complementary power to detect diseases such as COVID-19 from chest radiographs. This approach facilitates a more holistic understanding of medical images, improving the ac- curacy and reliability of diagnostic systems. To further enhance image classification, I explore hybrid architectures that combine convolutional and transformer-based models. These models leverage the strengths of both architectures, capturing fine-grained visual details and long-range dependencies. This significantly improves various medical imaging datasets, offering deeper interpretability and superior classification accuracy, particularly in complex diagnostic scenarios. Moving beyond classification, I tackle the fundamen- tal challenge of segmenting complex and irregular regions within medical images, where traditional deep learning models often struggle. To overcome this, I introduce a novel segmentation framework that combines the power of deep neural networks with trainable morphological operations. This leads to a more precise delineation of regions of inter- est, even in challenging clinical scenarios, setting a new benchmark for medical image segmentation. One of the most pressing issues in medical imaging is the inefficiency of current multitask learning models, which often require vast computational resources and struggle to generalize across different tasks. I present a lightweight multitask learn- ing framework that excels at both segmentation and classification, particularly in breast tumor analysis. Using novel morphological attention mechanisms and the sharing of task- specific knowledge, proposed model significantly reduces computational complexity while improving performance. Importantly, this framework demonstrates versatility across various medical imaging domains, from gland segmentation and malignancy detection in histology images to skin lesion analysis, demonstrating its robustness and applicability in real-world settings. Altogether, this thesis offers solutions to some of the most pressing problems in medical image analysis, providing models that are not only more accurate but also computationally efficient, making them suitable for deployment in clinical practice. |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Medical Image Analysis |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Medical Image Classification |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Hybrid Architectures |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Vision Transformer (ViT) |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Convolutional Neural Networks (CNNs) |
| 650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Discrete Wavelet Transform |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://dspace.isical.ac.in/jspui/handle/10263/7597">https://dspace.isical.ac.in/jspui/handle/10263/7597</a> |
| Link text |
Full text |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
THESIS |
