HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation

In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. State Space Models (SSMs), such as Mamba, have been recognized as a promising method. They not only demonstrate superior performance in modeling long-range interactions, but also preserve a linear computational complexity. The hybrid mechanism of SSM (State Space Model) and Transformer, after meticulous design, can enhance its capability for efficient modeling of visual features. Extensive experiments have demonstrated that integrating the self-attention mechanism into the hybrid part behind the layers of Mamba's architecture can greatly improve the modeling capacity to capture long-range spatial dependencies. In this paper, leveraging the hybrid mechanism of SSM, we propose a U-shape architecture model for medical image segmentation, named Hybird Transformer vision Mamba UNet (HTM-UNet). We conduct comprehensive experiments on the ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir, CVC-ColonDB, ETIS-Larib PolypDB public datasets and ZD-LCI-GIM private dataset. The results indicate that HTM-UNet exhibits competitive performance in medical image segmentation tasks. Our code is available at https://github.com/simzhangbest/HMT-Unet.

SAM2-PATH: A better segment anything model for semantic segmentation in digital pathology

The semantic segmentation task in pathology plays an indispensable role in assisting physicians in determining the condition of tissue lesions. Foundation models, such as the SAM (Segment Anything Model) and SAM2, exhibit exceptional performance in instance segmentation within everyday natural scenes. SAM-PATH has also achieved impressive results in semantic segmentation within the field of pathology. However, in computational pathology, the models mentioned above still have the following limitations. The pre-trained encoder models suffer from a scarcity of pathology image data; SAM and SAM2 are not suitable for semantic segmentation. In this paper, we have designed a trainable Kolmogorov-Arnold Networks(KAN) classification module within the SAM2 workflow, and we have introduced the largest pretrained vision encoder for histopathology (UNI) to date. Our proposed framework, SAM2-PATH, augments SAM2's capability to perform semantic segmentation in digital pathology autonomously, eliminating the need for human provided input prompts. The experimental results demonstrate that, after fine-tuning the KAN classification module and decoder, Our dataset has achieved competitive results on publicly available pathology data. The code has been open-sourced and can be found at the following address: https://github.com/simzhangbest/SAM2PATH.