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Convolutional Neural Networks (CNNs) and Transformer-based self-attention models have become standard for medical image segmentation. This paper demonstrates that convolution and self-attention, while widely used, are not the only effective methods for segmentation. Breaking with convention, we present a Convolution and self-Attention Free Mamba-based semantic Segmentation Network named CAF-MambaSegNet. Specifically, we design a Mamba-based Channel Aggregator and Spatial Aggregator, which are applied independently in each encoder-decoder stage. The Channel Aggregator extracts information across different channels, and the Spatial Aggregator learns features across different spatial locations. We also propose a Linearly Interconnected Factorized Mamba (LIFM) Block to reduce the computational complexity of a Mamba and to enhance its decision function by introducing a non-linearity between two factorized Mamba blocks. Our goal is not to outperform state-of-the-art results but to show how this innovative, convolution and self-attention-free method can inspire further research beyond well-established CNNs and Transformers, achieving linear complexity and reducing the number of parameters. Source code and pre-trained models will be publicly available.

We propose a novel multi-stage trans-dimensional architecture for multi-view cardiac image segmentation. Our method exploits the relationship between long-axis (2D) and short-axis (3D) magnetic resonance (MR) images to perform a sequential 3D-to-2D-to-3D segmentation, segmenting the long-axis and short-axis images. In the first stage, 3D segmentation is performed using the short-axis image, and the prediction is transformed to the long-axis view and used as a segmentation prior in the next stage. In the second step, the heart region is localized and cropped around the segmentation prior using a Heart Localization and Cropping (HLC) module, focusing the subsequent model on the heart region of the image, where a 2D segmentation is performed. Similarly, we transform the long-axis prediction to the short-axis view, localize and crop the heart region and again perform a 3D segmentation to refine the initial short-axis segmentation. We evaluate our proposed method on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) dataset, where our method outperforms state-of-the-art methods in segmenting cardiac regions of interest in both short-axis and long-axis images. The pre-trained models, source code, and implementation details will be publicly available.

Brain tumors are an abnormal growth of cells in the brain. They can be classified into distinct grades based on their growth. Often grading is performed based on a histological image and is one of the most significant predictors of a patients prognosis, the higher the grade, the more aggressive the tumor. Correct diagnosis of a tumor grade remains challenging. Though histopathological grading has been shown to be prognostic, results are subject to interobserver variability, even among experienced pathologists. Recently, the World Health Organization reported that advances in molecular genetics have led to improvements in tumor classification. This paper seeks to integrate histological images and genetic data for improved computer-aided diagnosis. We propose a novel Multi-modal Outer Arithmetic Block (MOAB) based on arithmetic operations to combine latent representations of the different modalities for predicting the tumor grade (Grade \rom{2}, \rom{3} and \rom{4}). Extensive experiments evaluate the effectiveness of our approach. By applying MOAB to The Cancer Genome Atlas (TCGA) glioma dataset, we show that it can improve separation between similar classes (Grade \rom{2} and \rom{3}) and outperform prior state-of-the-art grade classification techniques.

Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation. We propose a novel strategy that performs segmentation using specialist networks that focus on a single anatomy (left ventricle, right ventricle, or myocardium). Given an input long-axis cardiac MR image, our method performs a ternary segmentation in the first stage to identify these anatomical regions, followed by cropping the original image to focus subsequent processing on the anatomical regions. The specialist networks are coupled through an attention mechanism that performs cross-attention to interlink features from different anatomies, serving as a soft relative shape prior. Central to our approach is an additive attention block (E-2A block), which is used throughout our architecture thanks to its efficiency.

The assessment of advanced generative large language models (LLMs) poses a significant challenge, given their heightened complexity in recent developments. Furthermore, evaluating the performance of LLM-based applications in various industries, as indicated by Key Performance Indicators (KPIs), is a complex undertaking. This task necessitates a profound understanding of industry use cases and the anticipated system behavior. Within the context of the automotive industry, existing evaluation metrics prove inadequate for assessing in-car conversational question answering (ConvQA) systems. The unique demands of these systems, where answers may relate to driver or car safety and are confined within the car domain, highlight the limitations of current metrics. To address these challenges, this paper introduces a set of KPIs tailored for evaluating the performance of in-car ConvQA systems, along with datasets specifically designed for these KPIs. A preliminary and comprehensive empirical evaluation substantiates the efficacy of our proposed approach. Furthermore, we investigate the impact of employing varied personas in prompts and found that it enhances the model's capacity to simulate diverse viewpoints in assessments, mirroring how individuals with different backgrounds perceive a topic.

Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily affects the joint's synovial tissue. It is a highly heterogeneous disease, with wide cellular and molecular variability observed in synovial tissues. Over the last two decades, the methods available for their study have advanced considerably. In particular, Immunohistochemistry stains are well suited to highlighting the functional organisation of samples. Yet, analysis of IHC-stained synovial tissue samples is still overwhelmingly done manually and semi-quantitatively by expert pathologists. This is because in addition to the fragmented nature of IHC stained synovial tissue, there exist wide variations in intensity and colour, strong clinical centre batch effect, as well as the presence of many undesirable artefacts present in gigapixel Whole Slide Images (WSIs), such as water droplets, pen annotation, folded tissue, blurriness, etc. There is therefore a strong need for a robust, repeatable automated tissue segmentation algorithm which can cope with this variability and provide support to imaging pipelines. We train a UNET on a hand-curated, heterogeneous real-world multi-centre clinical dataset R4RA, which contains multiple types of IHC staining. The model obtains a DICE score of 0.865 and successfully segments different types of IHC staining, as well as dealing with variance in colours, intensity and common WSIs artefacts from the different clinical centres. It can be used as the first step in an automated image analysis pipeline for synovial tissue samples stained with IHC, increasing speed, reproducibility and robustness.