Accurate segmentation of small abnormal regions or anatomical structures in medical images, such as brain tumors and inferior alveolar nerve canals, is crucial for early disease diagnosis and surgical treatments. Most convolutional neural network-based medical image segmentation models still struggle with segmenting small objects due to limited pixel information and unclear features. To address this issue, we propose a method for small medical object segmentation base on multi-scale feature fusion and two-stage joint learning. In the first stage, a coarse segmentation network extracts multi-level features from the medical image and aggregates multi-scale high-level features to obtain a coarse-scaled result that includes location information about the objects. Then, a spatial transformation module passes the output of the coarse-scaled stage to the fine-scaled stage pixel-wise spatial weighting. In the second stage, a shallow denoising module utilizes deep features as guidance for shallow ones to suppress background noise. This results in clearer multi-scale features that enable accurate segmentation mask generation for small target. The proposed method was tested on three small object segmentation tasks, achieving top-ranking metrics such as mean Dice. These results demonstrate the superiority of our method over other state-of-the-art methods. The code is available at https://github.com/wth-stack/MFTN .