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.
arXiv:2506.06104v2 Announce Type: replace-cross Abstract: The rising prevalence of chronic wounds, especially in aging populations, presents a significant healthcare challenge due to prolonged hospit...
The paper presents AppendiGrade, an XAI‑enhanced deep learning framework that automatically detects complicated appendicitis from ultrasound images. Using a dataset of 4,679 images across five classes, the authors trained four pretrained models and achieved a best accuracy of 95.58% with InceptionV3 after applying preprocessing, hyperparameter tuning, and image sharpening. Grad‑CAM heatmaps were generated to explain the model’s predictions, facilitating easier expert cross‑checking.
LUTSeg is a new longitudinal dataset for chronic ulcer tissue segmentation, containing 141 images from 39 patients with wound masks and five tissue categories annotated by five expert clinicians. The dataset includes a multi‑expert gold‑standard subset for inter‑rater agreement analysis. The authors also propose TiSage, a semi‑supervised segmentation framework that uses multi‑scale semantic priors from a frozen medical vision‑language model within a teacher‑student architecture, and demonstrate its effectiveness on LUTSeg and DFUTissue.
The study compares five pre‑trained convolutional neural networks—ResNet50, VGG16, VGG19, MobileNet, and InceptionV3—for melanoma detection using dermatoscopic and histopathological image datasets. Accuracy varied across models and modalities, with ResNet50 achieving the highest scores (84% on HAM10000 and 83% on CR‑AI4SkIN) and InceptionV3 the lowest (71% on ISIC 2018). The results show that a model’s performance on dermatoscopic images does not necessarily predict its performance on histopathological images.
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.
arXiv:2608. 20154v1 Announce Type: new Abstract: Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes.
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%.
Wound3DAssist is a practical framework that creates 3D wound models from short handheld videos taken with consumer‑grade devices, enabling non‑contact, automatic measurements of wound surfaces. The system integrates 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. Evaluations on digital models, silicone phantoms, and real patients show millimeter‑scale reconstruction accuracy and multi‑view tissue composition analysis, with full assessments completed in under 20 minutes.
BruNet is a new cross‑domain transfer framework for automatic bruise segmentation that combines a ViT‑based visual encoder (either self‑supervised DINOv3 or pretrained LingBot‑Vision) with a SAM‑based mask decoder. The model is trained on the HAM10000 skin lesion dataset and evaluated on a separate bruise dataset without any fine‑tuning, achieving superior performance over CNN‑based models, state‑of‑the‑art segmentation models, ChatGPT‑4o/5‑assisted SAM2 zero‑shot baselines, and the medical‑oriented MedSAM. This work represents the first study to address pixel‑level localisation of bruises, demonstrating strong cross‑domain generalisation.
arXiv:2608. 15915v1 Announce Type: cross Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination.
arXiv:2607. 04486v1 Announce Type: new Abstract: Diagnosing and monitoring diseases frequently involves the analysis of human biological samples, with blood analysis being pivotal.
arXiv:2603.26483v2 Announce Type: replace Abstract: Medical edge-AI systems must operate under a difficult tension: delivering reliable diagnostic inference while running on devices with limited batt...