CLIP-TNseg: A Multi-Modal Hybrid Framework for Thyroid Nodule Segmentation in Ultrasound Images

Thyroid nodule segmentation in ultrasound images is crucial for accurate diagnosis and treatment planning. However, existing methods face challenges in segmentation accuracy, interpretability, and generalization, which hinder their performance. This letter proposes a novel framework, CLIP-TNseg, to address these issues by integrating a multimodal large model with a neural network architecture. CLIP-TNseg consists of two main branches: the Coarse-grained Branch, which extracts high-level semantic features from a frozen CLIP model, and the Fine-grained Branch, which captures fine-grained features using U-Net style residual blocks. These features are fused and processed by the prediction head to generate precise segmentation maps. CLIP-TNseg leverages the Coarse-grained Branch to enhance semantic understanding through textual and high-level visual features, while the Fine-grained Branch refines spatial details, enabling precise and robust segmentation. Extensive experiments on public and our newly collected datasets demonstrate its competitive performance. Our code and the original dataset are available at https://github.com/jayxjsun/CLIP-TNseg.

TableGPT2: A Large Multimodal Model with Tabular Data Integration

The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data remains notably underdeveloped, despite its foundational role in numerous real-world domains. This gap is critical for three main reasons. First, database or data warehouse data integration is essential for advanced applications; second, the vast and largely untapped resource of tabular data offers immense potential for analysis; and third, the business intelligence domain specifically demands adaptable, precise solutions that many current LLMs may struggle to provide. In response, we introduce TableGPT2, a model rigorously pre-trained and fine-tuned with over 593.8K tables and 2.36M high-quality query-table-output tuples, a scale of table-related data unprecedented in prior research. This extensive training enables TableGPT2 to excel in table-centric tasks while maintaining strong general language and coding abilities. One of TableGPT2's key innovations is its novel table encoder, specifically designed to capture schema-level and cell-level information. This encoder strengthens the model's ability to handle ambiguous queries, missing column names, and irregular tables commonly encountered in real-world applications. Similar to visual language models, this pioneering approach integrates with the decoder to form a robust large multimodal model. We believe the results are compelling: over 23 benchmarking metrics, TableGPT2 achieves an average performance improvement of 35.20% in the 7B model and 49.32% in the 72B model over prior benchmark-neutral LLMs, with robust general-purpose capabilities intact.