Description

Book Synopsis

.- Medical Image Segmentation.
.- TransE2UNet: Edge Guided TransEfficientUNET for Generalized Colon Polyp Segmentation from Endoscopy Images.
.- CA-Seg: An Attribute-based Medical Image Segmentation Framework for Unified Out-of-distributed Medical Image Segmentation.
.- TotalSegmentator 2D: A Tool for Rapid Anatomical Structure Analysis.
.- Promptable Cancer Segmentation Using Minimal Expert-curated Data.
.- SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours.
.- Semantic Segmentation with Spreading Scribbles.
.- A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology.
.- Exploring Context-Switching in Medical Image Retrieval Using Segmentation Models.
.- Segmentation in Histopathology Utilising Simulated Masked Patches.
.- A Feature-Driven Acquisition Strategy Using Scale-Invariant Descriptors for Deep Active Learning in Preclinical CT Segmentation.
.- Quantifying Inter-Annotator Agreement and Generalist Model Limitations in Imaging Mass Cytometry Single Cell Segmentation.
.- Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer.
.- DRASU-Net: Dual-backbone and Residual Atrous Squeeze module-aided U-Net Model for Polyp Segmentation.
.- PolypDINO: Adapting DINOv2 for Domain Generalized Polyp Segmentation.
.- Intraoperative Segmentation Through Deep Learning and Mask Post-processing in Laparoscopic Liver Surgery.
.- Retinal and Vascular Image Analysis.
.- Hessian-based Deep Retinal Vessel Segmentation with Extremely Few Annotations.
.- Diffusion with Adversarial Fine-Tuning for Improving Rare Retinal Disease Diagnosis.
.- Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage.
.- Enhanced Coronary Artery Segmentation in CTCA Using Bridging Centreline Integration.
.- QD-RetNet: Efficient Retinal Disease Classification via Quantized Knowledge Distillation.
.- Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis.
.- GenVOG: A Diffusion Probabilistic Framework for Patient-Independent Pose-Guided Nystagmus Video-Oculography (VOG) Generation.
.- Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans.

Medical Image Understanding and Analysis

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    A Paperback by Sharib Ali

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      Publisher: Springer
      Publication Date: Publication Date: 13/08/2025
      ISBN13: 9783031986932, 978-3031986932
      ISBN10:
      Also in:
      Computing Computer vision

      Description

      Book Synopsis

      .- Medical Image Segmentation.
      .- TransE2UNet: Edge Guided TransEfficientUNET for Generalized Colon Polyp Segmentation from Endoscopy Images.
      .- CA-Seg: An Attribute-based Medical Image Segmentation Framework for Unified Out-of-distributed Medical Image Segmentation.
      .- TotalSegmentator 2D: A Tool for Rapid Anatomical Structure Analysis.
      .- Promptable Cancer Segmentation Using Minimal Expert-curated Data.
      .- SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours.
      .- Semantic Segmentation with Spreading Scribbles.
      .- A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology.
      .- Exploring Context-Switching in Medical Image Retrieval Using Segmentation Models.
      .- Segmentation in Histopathology Utilising Simulated Masked Patches.
      .- A Feature-Driven Acquisition Strategy Using Scale-Invariant Descriptors for Deep Active Learning in Preclinical CT Segmentation.
      .- Quantifying Inter-Annotator Agreement and Generalist Model Limitations in Imaging Mass Cytometry Single Cell Segmentation.
      .- Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer.
      .- DRASU-Net: Dual-backbone and Residual Atrous Squeeze module-aided U-Net Model for Polyp Segmentation.
      .- PolypDINO: Adapting DINOv2 for Domain Generalized Polyp Segmentation.
      .- Intraoperative Segmentation Through Deep Learning and Mask Post-processing in Laparoscopic Liver Surgery.
      .- Retinal and Vascular Image Analysis.
      .- Hessian-based Deep Retinal Vessel Segmentation with Extremely Few Annotations.
      .- Diffusion with Adversarial Fine-Tuning for Improving Rare Retinal Disease Diagnosis.
      .- Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage.
      .- Enhanced Coronary Artery Segmentation in CTCA Using Bridging Centreline Integration.
      .- QD-RetNet: Efficient Retinal Disease Classification via Quantized Knowledge Distillation.
      .- Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis.
      .- GenVOG: A Diffusion Probabilistic Framework for Patient-Independent Pose-Guided Nystagmus Video-Oculography (VOG) Generation.
      .- Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans.

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