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GUMP-Net: An interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation

Pelvic segmentation is one of the most important and fundamental research problems in precise and intelligent diagnosis and treatment, as well as surgical planning and navigation for pelvic fractures. By combining an improved geodesic active contour model with deep neural networks, we propose GUMP-Net, an interpretable model-data-driven intelligent algorithm for multi-class pelvic segmentation, in which three network modules are designed to constitute the overall segmentation framework together: the object detection module for automatic level set initialization, the edge detector module for learning an anatomy-aware edge detector function and the iteration module for deep level set evolution.

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arXiv Computer Vision
Sep 11

Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification

The paper introduces two YOLOv11-based instance segmentation models that simultaneously perform wound boundary segmentation and wound classification across five clinically relevant wound types. Using a balanced dataset of 2,963 annotated images and data augmentation, the models achieve high performance, with YOLOv11x excelling in boundary segmentation and YOLOv11m and YOLOv11l leading in classification metrics. The lightweight YOLOv11n variant offers comparable accuracy with lower computational demands, making it suitable for resource-constrained clinical and remote care deployments.

By Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Igor Melnychuk, Ming C. Leu
computer-vision
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arXiv AI
Jun 17

High-Fidelity 3D Geometric Reconstruction of Pelvic Organs from MRI: A Hybrid Deep Learning and Iterative Optimization Approach

arXiv:2606. 17836v1 Announce Type: cross Abstract: Patient-specific 3D reconstruction of pelvic organ geometry from MRI is important for pelvic floor modeling and downstream patient-specific analysis.

By Hui Wang, Xiaowei Li, Chenxin Zhang, Yifan Feng, Jianwei Zuo, Yumeng Tang, Xiuli Sun, Jianliu Wang, Bing Xie, Jiajia Luo
computer-vision
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arXiv Computer Vision
Sep 7

Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function

The paper introduces a Deep Active Contour and Mean Curvature (DACMC) loss function for medical image segmentation. By incorporating mean curvature as a geometric constraint and approximating it with a convolution kernel, the method aims to improve the geometric characterization of segmented regions. Experiments on liver and spleen datasets show that DACMC achieves new state‑of‑the‑art performance across several segmentation benchmarks.

By Xiao-qiang Zhai, Zhi-feng Pang, Peng Zheng, Ze-wen Li, Yan-zhe Hou
computer-visionbenchmarks
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arXiv Computer Vision
Sep 4

Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

The paper presents a two‑pipeline framework for retinal fundus analysis that combines four‑class disease classification with vessel segmentation. It fine‑tunes eight ImageNet‑pretrained CNNs on the FIVES dataset, applies five gradient‑based explanation methods to assess model interpretability, and benchmarks ten U‑Net variants—including transformer‑based and attention‑enhanced architectures—on the FIVES and DRIVE datasets. The best classification results come from ResNet101 (94.17% accuracy), while the strongest segmentation performance is achieved by Attention U‑Net with a ResNet101V2 backbone, improving DRIVE IoU from 60.80% to 64.83%.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath, Asif Iftekher Fahim, Faisal Muhammad Shah
llmscomputer-visionfine-tuning
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arXiv Machine Learning
Jul 14

BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography

arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.

By Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan, Kah Ong Michael Goh, S M Hasan Mahmud, Raihana Zannat
llmscomputer-visionbenchmarks
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arXiv AI
Aug 28

Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures

The study evaluates four deep‑learning segmentation architectures—Unet, PSPNet, Linknet, and FPN—paired with six pre‑trained encoders to predict COVID‑19 lesions in CT images. Experiments on three COVID‑19 CT datasets show high accuracy, achieving a maximum binary F1‑score of 98% and multi‑class F1‑scores of 75% and 77%. The work aims to provide a standardized performance benchmark for medical image segmentation and a reference for other imaging scenarios.

By Sarmad Khan, Basim Azam, Arslan Shaukat
computer-visionbenchmarks
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