SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues

Weakly-supervised medical image segmentation is a challenging task that aims to reduce the annotation cost while keep the segmentation performance. In this paper, we present a novel framework, SimTxtSeg, that leverages simple text cues to generate high-quality pseudo-labels and study the cross-modal fusion in training segmentation models, simultaneously. Our contribution consists of two key components: an effective Textual-to-Visual Cue Converter that produces visual prompts from text prompts on medical images, and a text-guided segmentation model with Text-Vision Hybrid Attention that fuses text and image features. We evaluate our framework on two medical image segmentation tasks: colonic polyp segmentation and MRI brain tumor segmentation, and achieve consistent state-of-the-art performance.

Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs

Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise. As noise cannot easily be discerned from image details, such as high-frequency signals, its presence leads to extra bits needed for compression. Since the emerging learned image compression paradigm enables end-to-end optimization of codecs, recent efforts were made to integrate denoising into the compression model, relying on clean image features to guide denoising. However, these methods exhibit suboptimal performance under high noise levels, lacking the capability to generalize across diverse noise types. In this paper, we propose a novel method integrating a multi-scale denoiser comprising of Self Organizing Operational Neural Networks, for joint image compression and denoising. We employ contrastive learning to boost the network ability to differentiate noise from high frequency signal components, by emphasizing the correlation between noisy and clean counterparts. Experimental results demonstrate the effectiveness of the proposed method both in rate-distortion performance, and codec speed, outperforming the current state-of-the-art.