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...
By Vanessa Borst, Anna Riedmann, Tassilo Dege, Konstantin M\"uller, Astrid Schmieder, Birgit Lugrin, Samuel Kounev
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.
By Fahad Ahammed, Omar Faruq Shikdar, Navid Zaman, Md Tahsin, Md. Nawab Yousuf Ali, Golam Sorwar
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.
By Karen Sanchez, Carlos Hinojosa, Albert A. \'Avila, Andrea C. Riano-Rojas, Diego H. Romero, Jenny C. P\'aez, Martina Llin\'as, Bernard Ghanem
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.
By Wagner Moreno Schmitz, Marco Antonio de Castro Barbosa, Thiago Magalh\~aes Amaral, Dalcimar Casanova, Jefferson Tales Oliva
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.
By Pietro Mascagni, Julia Alekseenko, Pooja P Jain, Marta Goglia, Andrea Balla, Ludovica Baldari, Gianfranco Silecchia, Claudio Fiorillo, Vincenzo Tondolo, Salvador Morales-Conde, Luigi Boni, Sergio Alfieri, Nicolas Padoy