The field of medical image analysis is moving towards the development of more accurate and reliable diagnostic tools. Researchers are exploring the use of deep learning techniques, such as few-shot learning and hybrid frameworks, to improve the detection of various diseases and conditions, including placental abruption, diabetic retinopathy, and atypical mitosis. These innovative approaches aim to reduce the reliance on physician experience and subjective bias, leading to more consistent and accurate diagnoses. Noteworthy papers in this area include: An Automatic Detection Method for Hematoma Features in Placental Abruption Ultrasound Images Based on Few-Shot Learning, which proposes a model that achieves a detection accuracy of 78%. Hybrid Deep Learning Framework for Enhanced Diabetic Retinopathy Detection, which introduces a framework that combines traditional feature extraction and deep learning to enhance DR detection. Precise classification of low quality G-banded Chromosome Images by reliability metrics and data pruning classifier, which improves the classification precision of chromosomes using proposed reliability thresholding metrics. Robust Atypical Mitosis Classification with DenseNet121, which presents a framework that integrates stain-aware augmentation and imbalance-aware learning for atypical mitosis classification.