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Accurate medical image segmentation can effectively assist disease diagnosis and treatment. While neural networks were often applied to solve the segmentation problem in recent computer-aided diagnosis, the metadata of patients was usually neglected. Motivated by this, we propose a medical image segmentation and diagnosis framework that takes advantage of both the image and the patient’s metadata, such as gender and age. We present MMY-NET: a new multi-modal network for simultaneous tumor segmentation and diagnosis exploiting patient metadata. Our architecture consists of three parts: a visual encoder, a text encoder, and a decoder with a self-attention block. Specifically, we design a text preprocessing block to embed metadata effectively, and the image features and text embedding features are then fused on several layers between the two encoders. Moreover, Interlaced Sparse Self-Attention is added to the decoder to further boost the performance. We apply our algorithm on 1 private dataset (ZJU2), and 1 private dataset (LISHUI) for zero-shot validation. Results show that our algorithm combined with metadata outperforms its counterpart without metadata by a large margin for basal cell carcinoma segmentation (14.3 % improvement of IoU and 8.5 % of Dice on the ZJU2 dataset, and 7.1 % IoU on the LIZHUI validation dataset). Additionally, we applied MMY-Net to 1 public segmentation dataset to demonstrate its general segmentation capability. MMY-Net outperforms the state-of-the-art methods on the GlaS dataset. [ABSTRACT FROM AUTHOR]