The field of medical image analysis is rapidly advancing, with a focus on developing innovative deep learning architectures and techniques for improving diagnosis and treatment of various diseases. Recent developments have shown a significant improvement in the accuracy and efficiency of medical image analysis, particularly in the detection and classification of brain tumors, breast cancer, and pancreatic cancer. The use of novel loss functions, attention mechanisms, and domain adaptation techniques has been shown to enhance the performance of deep learning models in these tasks. Furthermore, the integration of multi-modal imaging and active learning strategies has demonstrated potential in reducing annotation costs and improving model generalizability. Noteworthy papers in this regard include ResLink, which proposed a novel deep learning architecture for brain tumor classification, and MobileDenseAttn, which introduced a dual-stream architecture for accurate and interpretable brain tumor detection. Additionally, the Mask-Guided Multi-Channel SwinUNETR Framework and the Deep Learning Framework for Early Detection of Pancreatic Cancer have shown promising results in breast cancer diagnosis and pancreatic cancer detection, respectively.