Volume 45, Issue 5

Cancer survival analysis aims to predict survival outcomes to evaluate the efficacy and prognosis of treatment. Although current approaches have designed diverse cross-modal learning methods to integrate genetic data and pathology images, they are frequently hindered by data redundancy. Pattern representation in high-dimensional genetic data remains a significant hurdle. Pathology data analysis is...

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Optical-resolution photoacoustic microscopy (OR-PAM) can visualize cellular-level wavelength-dependent optical absorption with high resolution and sensitivity. However, the imaging speed of OR-PAM has been limited by the laser repetition rate due to the point-by-point scanning of a focused laser beam. To overcome this limitation, we propose multifocal optical-resolution photoacoustic microscopy (M...

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Whole slide imaging (WSI) provides rich tissue information at a gigapixel resolution, posing significant challenges for the development of pathology analysis algorithms. Mainstream approaches effectively analyze WSI but treat the pretrained feature extractor and the task-specific network as independent modules, thereby restricting downstream task accuracy due to the limitations of the pretrained m...

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Positron emission tomography (PET) image synthesis is a highly ill-posed problem that requires auxiliary priors to 1) alleviate the loss of high-quality (HQ) information in low-quality (LQ) inputs, and 2) impose additional constraints to reduce mapping uncertainty. However, existing auxiliary priors in PET image synthesis often provide inadequate guidance due to inaccurate prior information or lim...

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Monolithic crystal PET detectors are equipped with depth of interaction (DOI) encoding, and neural network (NN) is reported as a useful tool for estimating DOI from light distribution (LD). However, obtaining an LD-DOI paired dataset is challenging when only perpendicular irradiation is available. To estimate DOI only from LD, we introduce a data-driven method called Wasserstein distance-based Loc...

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Self-supervised masked image modeling (MIM) methods have shown promising performances on analyzing natural images. However, directly applying such methods to medical image segmentation tasks still cannot achieve satisfactory results. The challenges arise from the facts that (i) medical images are inherently more complex compared to natural images, and the subjects in medical images often exhibit m...

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Multimodal MR image synthesis aims to generate missing modality images by effectively fusing and mapping from a subset of available MRI modalities. Most existing methods adopt an image-to-image translation paradigm, treating multiple modalities as input channels. However, these approaches often yield sub-optimal results due to the inherent difficulty in achieving precise feature- or semantic-level...

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Sepsis-induced brain dysfunction (SIBD), a critical determinant of mortality and long-term neurological sequelae in sepsis patients. The mechanistic understanding of SIBD has been limited by conventional single-modality approaches, which fail to capture the complex oxygenation-metabolism interplay. Here, we present an integrated photoacoustic-metabolomic platform that combines high-resolution phot...

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High-quality fundus images are critical for clinical diagnosis, yet real-world acquisition challenges often introduce multi-component degradations. Current deep learning methods typically address single degradations, lacking a unified handling of complex scenarios. In this paper, we propose the Multi-degradation Fundus Image Restoration Network (MFR-Net), an all-in-one restoration framework integr...

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Semi-supervised learning (SSL) has emerged as a promising paradigm for medical image segmentation, aiming to alleviate the scarcity of high-quality annotations by combining limited labeled and abundant unlabeled data. However, existing SSL methods suffer from inherent limitations: 1) consistency regularization overly relies on enforcing prediction consistency under different perturbations, neglect...

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Computer vision-based technologies significantly enhance surgical automation by advancing tool tracking, detection, and localization. However, Current data-driven approaches are data-voracious, requiring large, high-quality labeled image datasets. Our Work introduces a novel dynamic Gaussian Splatting technique to address the data scarcity in surgical image datasets. We propose a dynamic Gaussian ...

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Accurate detection of nuclei is a crucial step in advancing pathology image analysis for disease diagnosis and treatment. However, significant domain discrepancies exist among pathology images, which severely degrade the performance of detection models. Despite promising results from existing domain adaptation approaches, they may overlook the detrimental impact of intra-domain variation (IDV) at ...

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Photon-counting detector based computed tomography (PCCT) has greatly advanced in recent years. However, spectral inconsistency, referring to inter-pixel variations in detected counts per energy bin, can easily leads to ring or band artifacts and inaccuracies in CT reconstructed images. This work proposes a novel physics-model based method to correct for spectral inconsistency by modeling it throu...

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Deep learning has the potential to address the bottleneck of conventional medical microwave tomography, which is ill-posed and has a high computation cost. However, current physics-guided deep learning methods may fail to capture the imaged object’s salient regions, resulting in misdiagnosis. A deep neural network inspired by the distorted Born iterative method (DBIM) is proposed to address this c...

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3-D medical imaging modalities, including CT and MRI, provide high-resolution views essential for precision medicine. However, the increasing volume and complexity of 3-D medical images challenge manual analysis, particularly in classification and segmentation tasks. Although deep learning has shown considerable promise, it struggles to characterize small-scale, low-contrast anatomical structures,...

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