Koishiyeva D 1

1. Automatic cancer nuclei segmentation on histological images: comparison study of deep learning methods
2. A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation
3. Acoustic Fault Diagnosis of Industrial Pumps Using Interpretable Deep Learning and SHAP Analysis
4. Analysis of Loss Functions for Colorectal Polyp Segmentation Under Class Imbalance †
5. Cell nuclei image segmentation using U-Net and DeepLabV3+ with transfer learning and regularization
6. Resilience of UNet-Based Models Under Adversarial Conditions in Medical Image Segmentation †
7. COMPARISON EVALUATION OF UNET-BASED MODELS WITH NOISE AUGMENTATION FOR BREAST CANCER SEGMENTATION ON ULTRASOUND IMAGES
8. Modification of U-Net with Pre-Trained ResNet-50 and Atrous Block for Polyp Segmentation: Model TASPP-UNet †
9. Comparative Analysis of the Predictive Risk Assessment Modeling Technique Using Artificial Intelligence
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