WoundAmbit is a framework that evaluates state‑of‑the‑art deep learning models for wound segmentation, benchmarking general‑purpose vision, medical imaging, and public wound challenge methods under standardized training, augmentation, and cross‑validation. The study finds transformer‑based TransNeXt offers the best generalizability, while all models run at least one image per second on a CPU and show strong interpretability and expert‑approved mask quality. The framework also introduces a reference‑object approach to convert masks into clinically relevant wound size estimates, demonstrating close alignment with expert annotations and integration into a custom telehealth system.
By Vanessa Borst, Timo Dittus, Tassilo Dege, Astrid Schmieder, Samuel Kounev
WoundAIssist is an AI‑driven mobile application designed to support remote chronic wound care for elderly patients. It allows patients to capture wound images and complete questionnaires at home, while physicians monitor progress through remote video consultations and on‑device deep‑learning segmentation. A usability study involving patients and dermatologists found the app to be highly usable, of good quality, and positively received for its AI‑driven wound recognition.
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
Handcrafted preprocessing is widely employed in automated dermoscopic analysis to suppress imaging artifacts and enhance lesion visibility. Nevertheless, its actual contribution to modern real-time mo...
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