{"product_id":"medical-image-understanding-and-analysis-9783031986932","title":"Medical Image Understanding and Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- Medical Image Segmentation.\u003cbr\u003e.- TransE2UNet: Edge Guided TransEfficientUNET for Generalized Colon Polyp Segmentation from Endoscopy Images.\u003cbr\u003e.- CA-Seg: An Attribute-based Medical Image Segmentation Framework for Unified Out-of-distributed Medical Image Segmentation.\u003cbr\u003e.- TotalSegmentator 2D: A Tool for Rapid Anatomical Structure Analysis.\u003cbr\u003e.- Promptable Cancer Segmentation Using Minimal Expert-curated Data.\u003cbr\u003e.- SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours.\u003cbr\u003e.- Semantic Segmentation with Spreading Scribbles.\u003cbr\u003e.- A Hybrid Transformer-Graph Model for Multi-Class Lymph Node Segmentation in Histopathology.\u003cbr\u003e.- Exploring Context-Switching in Medical Image Retrieval Using Segmentation Models.\u003cbr\u003e.- Segmentation in Histopathology Utilising Simulated Masked Patches.\u003cbr\u003e.- A Feature-Driven Acquisition Strategy Using Scale-Invariant Descriptors for Deep Active Learning in Preclinical CT Segmentation.\u003cbr\u003e.- Quantifying Inter-Annotator Agreement and Generalist Model Limitations in Imaging Mass Cytometry Single Cell Segmentation.\u003cbr\u003e.- Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer.\u003cbr\u003e.- DRASU-Net: Dual-backbone and Residual Atrous Squeeze module-aided U-Net Model for Polyp Segmentation.\u003cbr\u003e.- PolypDINO: Adapting DINOv2 for Domain Generalized Polyp Segmentation.\u003cbr\u003e.- Intraoperative Segmentation Through Deep Learning and Mask Post-processing in Laparoscopic Liver Surgery.\u003cbr\u003e.- Retinal and Vascular Image Analysis.\u003cbr\u003e.- Hessian-based Deep Retinal Vessel Segmentation with Extremely Few Annotations.\u003cbr\u003e.- Diffusion with Adversarial Fine-Tuning for Improving Rare Retinal Disease Diagnosis.\u003cbr\u003e.- Deep Learning for Cardiovascular Risk Assessment: Proxy Features from Carotid Sonography as Predictors of Arterial Damage.\u003cbr\u003e.- Enhanced Coronary Artery Segmentation in CTCA Using Bridging Centreline Integration.\u003cbr\u003e.- QD-RetNet: Efficient Retinal Disease Classification via Quantized Knowledge Distillation.\u003cbr\u003e.- Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis.\u003cbr\u003e.- GenVOG: A Diffusion Probabilistic Framework for Patient-Independent Pose-Guided Nystagmus Video-Oculography (VOG) Generation.\u003cbr\u003e.- Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53195505598807,"sku":"9783031986932","price":66.49,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/medical-image-understanding-and-analysis-9783031986932","provider":"Book Curl","version":"1.0","type":"link"}