Background/Objective: The rising global incidence of skin cancer emphasizes the urgent need for reliable and accurate diagnostic tools to aid early intervention. This study introduces YOLOSAMIC (YOLO and SAM in Cancer Imaging), a fully automated segmentation framework that integrates YOLOv8 for lesion detection, and the Segment Anything Model (SAM)-Box for precise segmentation. The objective is to develop a reliable segmentation system that handles complex skin lesion characteristics without requiring manual intervention. Methods: A hybrid database comprising 3463 public and 765 private dermoscopy images was built to enhance model generalizability. YOLOv8 was employed to localize lesions through bounding box detection, while SAM-Box refined the segmentation process. The model was trained and evaluated under four scenarios to assess its robustness. Additionally, an ablation study examined the impact of grayscale conversion, image blur, and model pruning on segmentation performance. Results: YOLOSAMIC demonstrated high segmentation accuracy, achieving Dice and Jaccard scores of 0.9399 and 0.9112 on the public database and 0.8990 and 0.8445 on the hybrid dataset. Conclusions: The proposed YOLOSAMIC framework provides a robust, fully automated solution for skin lesion segmentation, eliminating the need for manual annotation. Integrating YOLOv8 and SAM-Box enhances segmentation precision, making it a valuable decision-support tool for dermatologists.

Aims: Breast cancer is the most common type of cancer in women and accounts for a large portion of cancer-related deaths. As in the other types of cancer, the prevention and early diagnosis of breast cancer gain importance day after day. For this purpose, the artificial intelligence-based decision support systems become popular in recent years. In this study, an automatic breast lesion segmentation process is proposed to detect breast lesions in the images taken with magnetic resonance imaging (MRI) protocol. Methods: Two most popular segmentation methods: expectation maximization (EM) and K-means algorithms are used to determine the region of breast lesions. Furthermore, superpixel based fuzzy C-means (SPFCM) algorithm is applied after EM and K-means methods to improve the lesion segmentation performance. Results: The proposed methods are evaluated on the private database constructed by the authors with ethical permission. The performances of the utilized methods are analyzed by comparing the lesion areas determined by a radiologist (ground-truth) and areas that are achieved by automatic segmentation algorithms. Conclusion: Dice coefficient, Jaccard index (JI), and area under curve (AUC) metrics are calculated for performance comparison. According to the simulation results, EM, K-means, EM+SPFCM, and K-means+SPFCM methods provides good segmentation performance on breast MRI database. The best segmentation results are obtained by using EM+SPFCM hybrid method. The results of the EM+SPFCM method are 0,8711, 0,8979, and 0,9981 for JI, Dice, and AUC, respectively.

In this paper, our goal is to determine the boundaries of lesion and then calculate the area of existing lesion in breast magnetic resonance (MR) images to provide a useful information to the radiologists. For this purpose, at first stage region growing (RG) method and active contour model (Snake) is applied to the images to make the boundaries of lesion visible. RG method is one of the simplest approaches for image segmentation and provides accurate results with lower computation time due to its seed point initialization step. Snake method molds a closed contour to the boundary of a region in an image and is also popular in medical image segmentation studies. In the presented study, both of these methods are utilized to determine the lesion boundaries. After determining the boundaries of lesion accurately in the second stage of the study, bit-quad method is applied to the segmented images. Bit quad method is used to compute the area and perimeter of binary lesion images based on matching the logical state of regions of image to binary patterns. Finally, to evaluate the performance of the proposed study, computer simulations are performed. It is demonstrated via computer simulations that the lesion area and parameter values are very close to real values. By means of this study it is aimed to support radiologists during diagnosis and assessment of breast lesions.

Skin lesion segmentation for recognizing and defining the boundaries of skin lesions in images is proper for automated analysis of skin lesion images, especially for the early diagnosis and detection of skin cancers. Deep learning architectures are an efficient way to implement segmentation once a skin lesion dataset is provided with ground truth images. This study evaluates deep learning architectures on a hybrid dataset, including a private dataset collected from a hospital and a public ISIC dataset. Four different test cases exist in the analysis where the combinations of public and private datasets are used as train and test datasets. Experimental results include Unet, Unet++, DeepLabV3, DeepLabV3++, and FPN segmentation architectures. According to the comparative evaluations, mixed datasets, where public and private datasets were used together, provided the best results. The evaluations also show that the collected dataset with ground truth images provided promising results.

Background/Objectives: Skin lesion classification remains challenging in real clinical settings because diagnostically relevant information is distributed across different imaging modalities, class imbalance is often severe, and overall performance alone may conceal important weaknesses in model behavior. In this study, a dual-view meta-aware model, termed DVM-SLC was developed for multi-class skin lesion classification on the MILK10k dataset. Methods: The model jointly processes paired clinical and dermoscopic images and incorporates patient-level contextual information through a dedicated metadata branch. Cross-view information is integrated by a gated fusion mechanism to preserve modality-specific representations while allowing adaptive interaction between the two views. The model was evaluated not only in terms of comparative classification performance, but also with respect to calibration, robustness under common image corruptions, and expert-supported explainability. Results: Among the configurations evaluated using pooled out-of-fold predictions, the gated DVM-SLC achieved the highest Accuracy (0.7002) and the highest observed Macro-AUC (0.8774), whereas the gate-free dual-view model with metadata achieved the highest Macro F1-score (0.4174). The accuracy difference between the gated model and the dermoscopic-only baseline, which provided the highest non-proposed Accuracy, was statistically significant, whereas the observed Macro-AUC difference was not statistically significant. These findings indicated a metric-dependent trade-off rather than uniform superiority of a single fusion strategy. The raw confidence estimates were already reasonably aligned with empirical correctness, and temperature scaling provided only a modest, fold-dependent improvement in aggregate calibration. In robustness analysis, the strongest degradation was observed under Gaussian noise, while the model remained comparatively stable under blur, JPEG compression, brightness variation, contrast variation, and color shift. The explainability analysis was complemented by a structured dermatologist review. Gradient-weighted Class Activation Mapping (Grad-CAM) outputs from the clinical and dermoscopic branches were examined to determine whether the highlighted regions localized the lesion and corresponded to clinically meaningful morphological or dermoscopic features. This assessment was qualitative and was not intended as quantitative localization validation. Conclusions: Overall, the findings present DVM-SLC as a broadly evaluated approach with metric-dependent strengths and clear limitations, particularly in minority-class recognition. Its contribution lies in combining comparative performance analysis with calibration, robustness, and structured qualitative explainability assessment.

Background/Objectives: Early identification of post-traumatic stress disorder (PTSD) after childhood sexual abuse may support timely assessment, but triage requires evaluation of calibration, missed cases, and referral workload. This study assessed whether routinely recorded information could support prioritization for PTSD-focused assessment. Methods: This retrospective single-center study evaluated classification of post-event diagnostic outcomes documented by a specialist child and adolescent psychiatrist in 311 forensic referral records. DSM-5 diagnoses were grouped as PTSD versus no recorded post-event psychiatric diagnosis (n = 218; PTSD = 106) and PTSD versus other non-PTSD clinical outcomes (n = 199; PTSD = 106). Five models were compared. Within each evaluation design, preprocessing, model selection, sigmoid calibration, and threshold derivation were confined to the development partition; each pipeline was applied unchanged to held-out or chronologically later records. Results: Histogram-based gradient boosting yielded a held-out ROC-AUC of 0.6190 (95% CI, 0.4472–0.7853) and PR-AUC of 0.6578 in the primary formulation. Its threshold flagged 36 of 44 records and captured 18 of 21 PTSD cases (sensitivity, 0.8571; specificity, 0.2174). LightGBM yielded a ROC-AUC of 0.6441 (95% CI, 0.4475–0.8125) and PR-AUC of 0.6805 in the secondary formulation. Its threshold flagged 30 of 40 records and captured 18 of 21 PTSD cases (sensitivity, 0.8571; specificity, 0.3684). Calibration reduced Brier scores to 0.2537 and 0.2401, but the calibrated primary model remained slightly worse than the prevalence-only reference (Brier skill score, −0.0165; calibration slope, 0.4276). Temporal ROC-AUC values were 0.6358 and 0.6767, with high workloads. Conclusions: Routinely recorded information provided modest discrimination, and high case capture required assessment of most records. External validation, prospective workflow evaluation, and local recalibration are required before clinical use.