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Needle localization in ultrasound images is pivotal for the successful execution of ultrasound-guided core needle biopsies. Automating the needle detection process can decrease the procedure time and lead to a more precise diagnosis. In this article, we introduce an automatic method for detecting the core needle and determining its trajectory in 2D ultrasound images.

This paper introduces a novel optimization approach for stain separation in digital histopathological images. Our stain separation cost function incorporates a smooth total variation regularization and is minimized by using a projected gradient algorithm. To enhance computational efficiency and enable supervised learning of the hyperparameters, we further unroll our algorithm into a neural network. The unrolled architecture is not only more efficient for solving the stain separation problem, but also allows to design a highly interpretable and flexible method.

Vision Transformer (ViT) has become a state-of-art in many vision tasks, owing to its great scalability and promising performance. In Magnetic Resonance Imaging (MRI), motion continues to be a major problem, which degrades the image quality and the corresponding disease assessment. The purpose of this work is to assess a ViT-based MRI motion correction method. Self-supervised learning was further incorporated to enhance the motion correction effects.

Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP). Therefore, it is crucial to effectively and efficiently deploy CNNs on devices with different computing capabil- ities to support computer-aided diagnosis. Many methods employ factorized convolutional layers to alleviate the bur- den of limited computational resources at the expense of expressiveness.

Bone marrow smear cytology involves observing and analyzing the morphological features of bone marrow cells, and identifying regions of interest (ROI) where the cells are morphologically clear and evenly distributed is a crucial part of this process. However, existing deep learning methods for selecting ROI in whole-slide images (WSI) of bone marrow smears have overlooked the unique characteristics of the smears, particularly the texture information, resulting in inadequate performance.

Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. In this work, we propose an accurate and efficient neural network for retinal vessel segmentation in OCTA images. The proposed network achieves accuracy comparable to other SOTA methods, while having fewer parameters and faster inference speed (e.g. 110x lighter and 1.3x faster than U-Net), which is very friendly for industrial applications. This is achieved by applying the modified Recurrent ConvNeXt Block to a full resolution convolutional network.

In the analysis of optical coherence tomography angiography (OCTA) images, the operation of segmenting specific targets is necessary. Existing methods typically train on supervised datasets with limited samples (approximately a few hundred), which can lead to overfitting. To address this, the low-rank adaptation technique is adopted for foundation model fine-tuning and proposed corresponding prompt point generation strategies to process various segmentation tasks on OCTA datasets. This method is named SAM-OCTA and has been experimented on the publicly available OCTA-500 dataset.

Single-source domain generalization in medical image segmentation is a challenging yet practical task, as domain shift commonly exists across medical datasets.
Previous works have attempted to alleviate this problem through adversarial data augmentation or random-style transformation.
However, these approaches neither fully leverage medical information nor consider the morphological structure alterations.
To address these limitations and enhance the fidelity and diversity of the augmented data,

Whole-slide image (WSI) classification is a challenging task because 1) patches from WSI lack annotation, and 2) WSI possesses unnecessary variability, e.g., stain protocol. Recently, Multiple-Instance Learning (MIL) has made significant progress, allowing for classification based on slide-level, rather than patch-level, annotations. However, existing MIL methods ignore that all patches from normal slides are normal. Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches.

Heterogeneity is present in Alzheimer’s disease (AD), making it challenging to study. To address this, we propose a graph neural network (GNN) approach to identify disease subtypes from magnetic resonance imaging (MRI) and functional MRI (fMRI) scans. Subtypes are identified by encoding the patients’ scans in brain graphs (via cortical similarity networks) and clustering the representations learnt by the GNN.