Medical image segmentation accurately identifies and delineates diagnostic regions, which is a crucial step in the early detection and accurate diagnosis of diseases. Diffusion models have demonstrated remarkable performance in preserving image details and structures by gradually adding noise followed by a reverse denoising process, making them widely explored in image segmentation tasks. In this study, we propose a novel approach for medical image segmentation utilizing diffusion models, termed MFSegDiff, which frames the segmentation task as an iterative denoising process. To address the inconsistency between image semantic features and noise embeddings, we introduce a Cross-Attention Alignment Module(CAAM). This module enhances the original image features and integrates noise and semantic information into the network through a linear attention mechanism. Additionally, we employ a Global-Local Multi-Frequency Module (GLMFM) to extract global contextual information from the spatial to the frequency domain by integrating multi-frequency features with local features. We evaluate the proposed on three datasets, including ISIC-2017, ISIC-2018, and ROSE. Results demonstrate strong generalization capabilities and achieve state-of-the-art segmentation performance, highlighting significant potential for clinical applications.

This paper proposes a spatial language generation system to communicate with a person about the location of an object in an indoor environment. It aims at finding a short, accurate and human-like description for building a natural and friendly interface between robots and humans using spatial language interaction. The system performs an inverse procedure to spatial language grounding which links natural commands to robot actions. The system works in two steps. It will first search for the best matching grounding model which describes the spatial relations between the target object and the references; then it will generate the natural language by mimicking a human's talking style. A corpus of 149 spatial language commands for an indoor environment fetch task is used to train the language generation model. An early-stage experiment is conducted and the results illustrate a potential for further development.

This paper proposes a system that allows the use of natural spatial language to control a robot performing a fetch task in an indoor environment. The system processes spatial referencing language and extracts a tree structure of language chunks. The spatial language system is then grounded to a robot navigation instruction in the form of a sequence of actions based on spatial references to furniture and room structure; the best navigation instruction is selected by scoring. In addition, the Reference-Direction-Target (RDT) model is proposed to represent indoor robot actions. To control the robot for the fetch task, a behavior model is designed based on the RDT model. An assistive robot has been designed and programmed based on this system. The proposed spatial language grounding model and robot behavior model are tested experimentally in three sets of experiments. Results show that the system enables a robot to follow spatial language commands in a physical indoor environment even if the referenced furniture items are re-positioned.

Methods and experimental results are presented for interpreting 3D spatial language descriptions used for human to robot communication in a fetch task. The work is based on human subject experiments in which spatial language descriptions were logged from younger and older adult participants. A spatial language model is proposed, and methods are presented for translating natural spatial language descriptions into robot commands that allow the robot to find the requested object. Robot command representation and robot behavior are also discussed. Experimental results compare path metrics of the robot system and human subjects in a common simulation environment. The overall success rate of the robot trials is 85%.

This paper outlines a new study that investigates spatial language for use in human-robot communication. The scenario studied is a home setting in which the elderly resident has misplaced an object, such as eyeglasses, and the robot will help the resident find the object. We present results from phase I of the study in which we investigate spatial language generated to a human addressee or a robot addressee in a virtual environment.