Medical Referring Image Segmentation via Next-Token Mask Prediction

Medical Referring Image Segmentation (MRIS) involves segmenting target regions in medical images based on natural language descriptions. While achieving promising results, recent approaches usually involve complex design of multimodal fusion or multi-stage decoders. In this work, we propose NTP-MRISeg, a novel framework that reformulates MRIS as an autoregressive next-token prediction task over a unified multimodal sequence of tokenized image, text, and mask representations. This formulation streamlines model design by eliminating the need for modality-specific fusion and external segmentation models, supports a unified architecture for end-to-end training. It also enables the use of pretrained tokenizers from emerging large-scale multimodal models, enhancing generalization and adaptability. More importantly, to address challenges under this formulation-such as exposure bias, long-tail token distributions, and fine-grained lesion edges-we propose three novel strategies: (1) a Next-k Token Prediction (NkTP) scheme to reduce cumulative prediction errors, (2) Token-level Contrastive Learning (TCL) to enhance boundary sensitivity and mitigate long-tail distribution effects, and (3) a memory-based Hard Error Token (HET) optimization strategy that emphasizes difficult tokens during training. Extensive experiments on the QaTa-COV19 and MosMedData+ datasets demonstrate that NTP-MRISeg achieves new state-of-the-art performance, offering a streamlined and effective alternative to traditional MRIS pipelines.

Short Wins Long: Short Codes with Language Model Semantic Correction Outperform Long Codes

This paper presents a novel semantic-enhanced decoding scheme for transmitting natural language sentences with multiple short block codes over noisy wireless channels. After ASCII source coding, the natural language sentence message is divided into segments, where each is encoded with short block channel codes independently before transmission. At the receiver, each short block of codewords is decoded in parallel, followed by a semantic error correction (SEC) model to reconstruct corrupted segments semantically. We design and train the SEC model based on Bidirectional and Auto-Regressive Transformers (BART). Simulations demonstrate that the proposed scheme can significantly outperform encoding the sentence with one conventional long LDPC code, in terms of block error rate (BLER), semantic metrics, and decoding latency. Finally, we proposed a semantic hybrid automatic repeat request (HARQ) scheme to further enhance the error performance, which selectively requests retransmission depends on semantic uncertainty.