Mengnan He

Wenjie Zhang, Liming Luo, Mengnan He et al.

Accurate organ segmentation is essential for clinical tasks such as radiotherapy planning and disease monitoring. Recent foundation models like MedSAM achieve strong results using point or bounding-box prompts but still require manual interaction. We propose DescriptorMedSAM, a lightweight extension of MedSAM that incorporates structured text prompts, ranging from simple organ names to combined shape and location descriptors to enable click-free segmentation. DescriptorMedSAM employs a CLIP text encoder to convert radiology-style descriptors into dense embeddings, which are fused with visual tokens via a cross-attention block and a multi-scale feature extractor. We designed four descriptor types: Name (N), Name + Shape (NS), Name + Location (NL), and Name + Shape + Location (NSL), and evaluated them on the FLARE 2022 dataset under zero-shot and few-shot settings, where organs unseen during training must be segmented with minimal additional data. NSL prompts achieved the highest performance, with a Dice score of 0.9405 under full supervision, a 76.31% zero-shot retention ratio, and a 97.02% retention ratio after fine-tuning with only 50 labeled slices per unseen organ. Adding shape and location cues consistently improved segmentation accuracy, especially for small or morphologically complex structures. We demonstrate that structured language prompts can effectively replace spatial interactions, delivering strong zero-shot performance and rapid few-shot adaptation. By quantifying the role of descriptor, this work lays the groundwork for scalable, prompt-aware segmentation models that generalize across diverse anatomical targets with minimal annotation effort.

Zhu Li, Yuqing Zhang, Mengxi Nie et al.

Recent advancements in end-to-end speech synthesis have made it possible to generate highly natural speech. However, training these models typically requires a large amount of high-fidelity speech data, and for unseen texts, the prosody of synthesized speech is relatively unnatural. To address these issues, we propose to combine a fine-tuned BERT-based front-end with a pre-trained FastSpeech2-based acoustic model to improve prosody modeling. The pre-trained BERT is fine-tuned on the polyphone disambiguation task, the joint Chinese word segmentation (CWS) and part-of-speech (POS) tagging task, and the prosody structure prediction (PSP) task in a multi-task learning framework. FastSpeech 2 is pre-trained on large-scale external data that are noisy but easier to obtain. Experimental results show that both the fine-tuned BERT model and the pre-trained FastSpeech 2 can improve prosody, especially for those structurally complex sentences.