85 papers
This paper proposes a novel transformer-based geometric deep learning model that tokenizes tetrahedral meshes with anatomical landmarks to accurately classify Alzheimer's disease and predict brain amyloid positivity in medium-risk individuals, offering a robust alternative to costly and invasive PET scans.
This paper proposes a novel, problem-agnostic diffusion model that enhances inverse problems by decomposing the conditional score function into an unconditional pretrained component and a Gaussian-prior-based MAP-guided term, resulting in superior content preservation and structural coherence compared to state-of-the-art methods.
The paper proposes PiVOT, a visual prompting mechanism that leverages a pretrained CLIP foundation model to automatically generate and refine online visual prompts, thereby enhancing generic object tracking by effectively suppressing distractors through contrastive guidance.
This paper proposes a deep learning-based approach utilizing UNET, Inception, and ResNet architectures to achieve automatic 2D and 3D glioma segmentation on BraTS datasets, demonstrating that a ResNet model effectively balances computational efficiency and spatial accuracy to significantly improve diagnosis with high accuracy and Dice scores.
This study proposes a high-accuracy glioma subclass classification framework that fuses 2D and 3D UNET-segmented multimodal MRI images using weighted averaging and classifies them via a pre-trained ResNet50 model, achieving a 99.25% accuracy rate.
This paper presents an innovative, mobile-device-based 3D imaging approach using Structure-from-Motion and segmentation algorithms to reconstruct and analyze aggregate size and shape from stockpiles, offering a convenient and affordable solution for onsite quality assurance in road construction and geotechnics.
This paper proposes a zero-AV-resource framework for audiovisual speech recognition that generates synthetic talking-head videos by lip-syncing static facial images with real audio, successfully enabling high-performance model training for under-resourced languages like Catalan without the need for labeled video corpora.
The paper introduces PANO, a physics-aware neural operator that performs direct, single-pass inversion of raw sensor data into high-quality 3D photoacoustic images, outperforming traditional algorithms and enabling real-time reconstruction across diverse sparse acquisition settings to facilitate the clinical translation of 3D PACT.
The paper introduces ZACH-ViT, a compact, permutation-invariant Vision Transformer that removes positional embeddings and the [CLS] token to achieve competitive few-shot performance on medical imaging datasets, demonstrating that architectural alignment with data-specific spatial priors can outweigh universal benchmark dominance in low-resource settings.
The paper introduces PanSubNet, an interpretable deep learning framework that accurately predicts clinically relevant basal-like and classical molecular subtypes of pancreatic ductal adenocarcinoma directly from routine H&E-stained histology slides, offering a cost-effective and rapid alternative to RNA sequencing for precision oncology.
This paper introduces a few-shot image fusion framework that leverages a Granular Ball Pixel Computation algorithm to generate adaptive, confidence-aware incomplete priors, enabling a lightweight neural network to learn effective fusion rules from minimal data without requiring real fused images for supervision.
GOUHFI 2.0 is an updated deep-learning toolbox that enables robust, contrast-agnostic whole-brain segmentation, cortical parcellation, and volumetry for Ultra-High Field MRI by utilizing two specialized 3D U-Net models trained on diverse datasets to overcome the limitations of existing tools.
This paper introduces 3D-PIUNet, a hybrid deep learning framework that enhances EEG source reconstruction by initializing a 3D U-Net with physics-informed pseudo-inverse solutions and refining them through learned data priors, thereby achieving superior spatial accuracy and practical applicability compared to traditional and end-to-end methods.
This paper presents a deep learning framework utilizing a convolutional neural network and a convolutional denoising autoencoder to accurately segment retinal layers and pigment epithelial detachments in novel low-cost full-field OCT images, thereby enabling automated computer-aided diagnosis for home-based monitoring of age-related macular degeneration.
This paper presents an efficient pipelined FPGA architecture with optimized memory organization for the displacement vector search module of JPEG XS's Intra Pattern Copy tool, achieving a throughput of 38.3 Mpixels/s at 277 mW to enable practical hardware deployment.
This paper introduces Diffusive INR (DINR), a novel framework that regularizes implicit neural representations with a diffusion prior trained on synthetic data to achieve high-quality, artifact-reduced 3D reconstructions of concrete microstructures from sparse-view neutron computed tomography, outperforming state-of-the-art methods under extreme data limitations.
This paper proposes an adaptive multimodal semantic transmission system for satellite communications that utilizes a dual-stream generative architecture and a large language model-based decision module to dynamically switch between audio and video streams, thereby achieving high-fidelity synchronized audiovisual reconstruction while significantly reducing bandwidth consumption under challenging channel conditions.
This paper introduces ARCHE, an end-to-end learned image compression framework that achieves state-of-the-art rate-distortion efficiency by unifying hierarchical, spatial, and channel-based priors with adaptive feature recalibration, all while maintaining computational efficiency without relying on recurrent or transformer-based components.
This paper introduces SAAIPAA, a physics-based framework that optimizes the placement of corner reflectors to execute aspect-angle-invariant physical adversarial attacks against SAR target recognition models, achieving high fooling rates even when the attacker lacks knowledge of the SAR platform's viewing angles.
This paper proposes an unsupervised medical anomaly detection method that augments CNN autoencoder latent representations with in-batch relational features via hypergraph estimation and graph convolution, significantly improving the separation of healthy anatomical variations from pathologies and reducing false positives on a heterogeneous brain tumor dataset.