pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data
Authors: Mengping Yang, Zhe Wang, Ziqiu Chi, Dongdong Li, Wenli Du
Date: 2025-02-02 00:00:00
Tasks: Data Augmentation

Generative adversarial networks (GANs) have made remarkable achievements in synthesizing images in recent years. Typically, training GANs requires massive data, and the performance of GANs deteriorates significantly when training data is limited. To improve the synthesis performance of GANs in low-data regimes, existing approaches use various data augmentation techniques to enlarge the training sets. However, it is identified that these augmentation techniques may leak or even alter the data distribution. To remedy this, we propose an adversarial semantic augmentation (ASA) technique to enlarge the training data at the semantic level instead of the image level. Concretely, considering semantic features usually encode informative information of images, we estimate the covariance matrices of semantic features for both real and generated images to find meaningful transformation directions. Such directions translate original features to another semantic representation, e.g., changing the backgrounds or expressions of the human face dataset. Moreover, we derive an upper bound of the expected adversarial loss. By optimizing the upper bound, our semantic augmentation is implicitly achieved. Such design avoids redundant sampling of the augmented features and introduces negligible computation overhead, making our approach computation efficient. Extensive experiments on both few-shot and large-scale datasets demonstrate that our method consistently improve the synthesis quality under various data regimes, and further visualized and analytic results suggesting satisfactory versatility of our proposed method.
Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer
Authors: Zhenyu Yu, Jinnian Wang
Date: 2025-02-02 00:00:00
Tasks: Style Transfer

Forests function as crucial carbon reservoirs on land, and their carbon sinks can efficiently reduce atmospheric CO2 concentrations and mitigate climate change. Currently, the overall trend for monitoring and assessing forest carbon stocks is to integrate ground monitoring sample data with satellite remote sensing imagery. This style of analysis facilitates large-scale observation. However, these techniques require improvement in accuracy. We used GF-1 WFV and Landsat TM images to analyze Huize County, Qujing City, Yunnan Province in China. Using the style transfer method, we introduced Swin Transformer to extract global features through attention mechanisms, converting the carbon stock estimation into an image translation.
A method for estimating forest carbon storage distribution density via artificial intelligence generated content model
Authors: Zhenyu Yu, Jinnian Wang
Date: 2025-02-02 00:00:00
Tasks: Knowledge Distillation

Forest is the most significant land-based carbon storage mechanism. The forest carbon sink can effectively decrease the atmospheric CO2 concentration and mitigate climate change. Remote sensing estimation not only ensures high accuracy of data, but also enables large-scale area observation. Optical images provide the possibility for long-term monitoring, which is a potential issue in the future carbon storage estimation research. We chose Huize County, Qujing City, Yunnan Province, China as the study area, took GF-1 WFV satellite image as the data, introduced the KD-VGG module to extract the initial features, and proposed the improved implicit diffusion model (IIDM). The results showed that: (1) The VGG-19 module after knowledge distillation can realize the initial feature extraction, reduce the inference time and improve the accuracy in the case of reducing the number of model parameters. (2) The Attention + MLP module was added for feature fusion to obtain the relationship between global and local features and realized the restoration of high-fidelity images in the continuous scale range. (3) The IIDM model proposed in this paper had the highest estimation accuracy, with RMSE of 28.68, which was 13.16 higher than that of the regression model, about 31.45%. In the estimation of carbon storage, the generative model can extract deeper features, and its performance was significantly better than other models. It demonstrated the feasibility of artificial intelligence-generated content (AIGC) in the field of quantitative remote sensing and provided valuable insights for the study of carbon neutralization effect. By combining the actual characteristics of the forest, the regional carbon storage estimation with a resolution of 16-meter was utilized to provide a significant theoretical basis for the formulation of forest carbon sink regulation.
Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images
Authors: Shengtian Sang, Hassan Jahanandish, Cynthia Xinran Li, Indrani Bhattachary, Jeong Hoon Lee, LiChun Zhang, Sulaiman Vesal, Pejman Ghanouni, Richard Fan, Geoffrey A. Sonn, Mirabela Rusu
Date: 2025-02-02 00:00:00
Tasks: Segmentation

Prostate cancer is a major cause of cancer-related deaths in men, where early detection greatly improves survival rates. Although MRI-TRUS fusion biopsy offers superior accuracy by combining MRI's detailed visualization with TRUS's real-time guidance, it is a complex and time-intensive procedure that relies heavily on manual annotations, leading to potential errors. To address these challenges, we propose a fully automatic MRI-TRUS fusion-based segmentation method that identifies prostate tumors directly in TRUS images without requiring manual annotations. Unlike traditional multimodal fusion approaches that rely on naive data concatenation, our method integrates a registration-segmentation framework to align and leverage spatial information between MRI and TRUS modalities. This alignment enhances segmentation accuracy and reduces reliance on manual effort. Our approach was validated on a dataset of 1,747 patients from Stanford Hospital, achieving an average Dice coefficient of 0.212, outperforming TRUS-only (0.117) and naive MRI-TRUS fusion (0.132) methods, with significant improvements (p $<$ 0.01). This framework demonstrates the potential for reducing the complexity of prostate cancer diagnosis and provides a flexible architecture applicable to other multimodal medical imaging tasks.
S2CFormer: Revisiting the RD-Latency Trade-off in Transformer-based Learned Image Compression
Authors: Yunuo Chen, Qian Li, Bing He, Donghui Feng, Ronghua Wu, Qi Wang, Li Song, Guo Lu, Wenjun Zhang
Date: 2025-02-02 00:00:00
Tasks: Image Compression

Transformer-based Learned Image Compression (LIC) suffers from a suboptimal trade-off between decoding latency and rate-distortion (R-D) performance. Moreover, the critical role of the FeedForward Network (FFN)-based channel aggregation module has been largely overlooked. Our research reveals that efficient channel aggregation-rather than complex and time-consuming spatial operations-is the key to achieving competitive LIC models. Based on this insight, we initiate the ``S2CFormer'' paradigm, a general architecture that simplifies spatial operations and enhances channel operations to overcome the previous trade-off. We present two instances of the S2CFormer: S2C-Conv, and S2C-Attention. Both models demonstrate state-of-the-art (SOTA) R-D performance and significantly faster decoding speed. Furthermore, we introduce S2C-Hybrid, an enhanced variant that maximizes the strengths of different S2CFormer instances to achieve a better performance-latency trade-off. This model outperforms all the existing methods on the Kodak, Tecnick, and CLIC Professional Validation datasets, setting a new benchmark for efficient and high-performance LIC. The code is at \href{https://github.com/YunuoChen/S2CFormer}{https://github.com/YunuoChen/S2CFormer}.
Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach
Authors: Priyam Ganguly, Akhilbaran Ghosh
Date: 2025-02-01 00:00:00
Tasks: Brain Tumor Classification, Computational Efficiency, Medical Image Analysis

Brain tumor classification using MRI images is critical in medical diagnostics, where early and accurate detection significantly impacts patient outcomes. While recent advancements in deep learning (DL), particularly CNNs, have shown promise, many models struggle with balancing accuracy and computational efficiency and often lack robustness across diverse datasets. To address these challenges, we propose a novel model architecture integrating separable convolutions and squeeze and excitation (SE) blocks, designed to enhance feature extraction while maintaining computational efficiency. Our model further incorporates batch normalization and dropout to prevent overfitting, ensuring stable and reliable performance. The proposed model is lightweight because it uses separable convolutions, which reduce the number of parameters, and incorporates global average pooling instead of fully connected layers to minimize computational complexity while maintaining high accuracy. Our model does better than other models by about 0.5% to 1.0% in accuracy and 1.5% to 2.5% in loss reduction, as shown by many experiments. It has a validation accuracy of 99.22% and a test accuracy of 98.44%. These results highlight the model's ability to generalize effectively across different brain tumour types, offering a robust tools for clinical applications. Our work sets a new benchmark in the field, providing a foundation for future research in optimizing the accuracy and efficiency of DL models for medical image analysis.
Deep Task-Based Beamforming and Channel Data Augmentations for Enhanced Ultrasound Imaging
Authors: Ariel Amar, Ahuva Grubstein, Eli Atar, Keren Peri-Hanania, Nimrod Glazer, Ronnie Rosen, Shlomi Savariego, Yonina C. Eldar
Date: 2025-02-01 00:00:00
Tasks: Lesion Classification

This paper introduces a deep learning (DL)-based framework for task-based ultrasound (US) beamforming, aiming to enhance clinical outcomes by integrating specific clinical tasks directly into the beamforming process. Task-based beamforming optimizes the beamformer not only for image quality but also for performance on a particular clinical task, such as lesion classification. The proposed framework explores two approaches: (1) a Joint Beamformer and Classifier (JBC) that classifies the US images generated by the beamformer to provide feedback for image quality improvement; and (2) a Channel Data Classifier Beamformer (CDCB) that incorporates classification directly at the channel data representation within the beamformer's bottleneck layer. Additionally, we introduce channel data augmentations to address challenges posed by noisy and limited in-vivo data. Numerical evaluations demonstrate that training with channel data augmentations significantly improves image quality. The proposed methods were evaluated against conventional Delay-and-Sum (DAS) and Minimum Variance (MV) beamforming techniques, demonstrating superior performance in terms of both image contrast and clinical relevance. Among all methods, the CDCB approach achieves the best results, outperforming others in terms of image quality and clinical relevance. These approaches exhibit significant potential for improving clinical relevance and image quality in ultrasound imaging.
A framework for river connectivity classification using temporal image processing and attention based neural networks
Authors: Timothy James Becker, Derin Gezgin, Jun Yi He Wu, Mary Becker
Date: 2025-02-01 00:00:00
Tasks: Image Augmentation, image-classification, Image Classification, Image Enhancement

Measuring the connectivity of water in rivers and streams is essential for effective water resource management. Increased extreme weather events associated with climate change can result in alterations to river and stream connectivity. While traditional stream flow gauges are costly to deploy and limited to large river bodies, trail camera methods are a low-cost and easily deployed alternative to collect hourly data. Image capturing, however requires stream ecologists to manually curate (select and label) tens of thousands of images per year. To improve this workflow, we developed an automated instream trail camera image classification system consisting of three parts: (1) image processing, (2) image augmentation and (3) machine learning. The image preprocessing consists of seven image quality filters, foliage-based luma variance reduction, resizing and bottom-center cropping. Images are balanced using variable amount of generative augmentation using diffusion models and then passed to a machine learning classification model in labeled form. By using the vision transformer architecture and temporal image enhancement in our framework, we are able to increase the 75% base accuracy to 90% for a new unseen site image. We make use of a dataset captured and labeled by staff from the Connecticut Department of Energy and Environmental Protection between 2018-2020. Our results indicate that a combination of temporal image processing and attention-based models are effective at classifying unseen river connectivity images.
Entropy-based measure of rock sample heterogeneity derived from micro-CT images
Authors: Luan Coelho Vieira Silva, Júlio de Castro Vargas Fernandes, Felipe Belilaqua Foldes Guimarães, Pedro Henrique Braga Lisboa, Carlos Eduardo Menezes dos Anjos, Thais Fernandes de Matos, Marcelo Ramalho Albuquerque, Rodrigo Surmas, Alexandre Gonçalves Evsukoff
Date: 2025-02-01 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

This study presents an automated method for objectively measuring rock heterogeneity via raw X-ray micro-computed tomography (micro-CT) images, thereby addressing the limitations of traditional methods, which are time-consuming, costly, and subjective. Unlike approaches that rely on image segmentation, the proposed method processes micro-CT images directly, identifying textural heterogeneity. The image is partitioned into subvolumes, where attributes are calculated for each one, with entropy serving as a measure of uncertainty. This method adapts to varying sample characteristics and enables meaningful comparisons across distinct sets of samples. It was applied to a dataset consisting of 4,935 images of cylindrical plug samples derived from Brazilian reservoirs. The results showed that the selected attributes play a key role in producing desirable outcomes, such as strong correlations with structural heterogeneity. To assess the effectiveness of our method, we used evaluations provided by four experts who classified 175 samples as either heterogeneous or homogeneous, where each expert assessed a different number of samples. One of the presented attributes demonstrated a statistically significant difference between the homogeneous and heterogeneous samples labelled by all the experts, whereas the other two attributes yielded nonsignificant differences for three out of the four experts. The method was shown to better align with the expert choices than traditional textural attributes known for extracting heterogeneous properties from images. This textural heterogeneity measure provides an additional parameter that can assist in rock characterization, and the automated approach ensures easy reproduction and high cost-effectiveness.
Segment Anything for Histopathology
Authors: Titus Griebel, Anwai Archit, Constantin Pape
Date: 2025-02-01 00:00:00
Tasks: Image Segmentation, Instance Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

Nucleus segmentation is an important analysis task in digital pathology. However, methods for automatic segmentation often struggle with new data from a different distribution, requiring users to manually annotate nuclei and retrain data-specific models. Vision foundation models (VFMs), such as the Segment Anything Model (SAM), offer a more robust alternative for automatic and interactive segmentation. Despite their success in natural images, a foundation model for nucleus segmentation in histopathology is still missing. Initial efforts to adapt SAM have shown some success, but did not yet introduce a comprehensive model for diverse segmentation tasks. To close this gap, we introduce PathoSAM, a VFM for nucleus segmentation, based on training SAM on a diverse dataset. Our extensive experiments show that it is the new state-of-the-art model for automatic and interactive nucleus instance segmentation in histopathology. We also demonstrate how it can be adapted for other segmentation tasks, including semantic nucleus segmentation. For this task, we show that it yields results better than popular methods, while not yet beating the state-of-the-art, CellViT. Our models are open-source and compatible with popular tools for data annotation. We also provide scripts for whole-slide image segmentation. Our code and models are publicly available at https://github.com/computational-cell-analytics/patho-sam.
Exploring Linear Attention Alternative for Single Image Super-Resolution
Authors: Rongchang Lu, Changyu Li, Donghang Li, Guojing Zhang, Jianqiang Huang, Xilai Li
Date: 2025-02-01 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, SSIM, Super-Resolution

Deep learning-based single-image super-resolution (SISR) technology focuses on enhancing low-resolution (LR) images into high-resolution (HR) ones. Although significant progress has been made, challenges remain in computational complexity and quality, particularly in remote sensing image processing. To address these issues, we propose our Omni-Scale RWKV Super-Resolution (OmniRWKVSR) model which presents a novel approach that combines the Receptance Weighted Key Value (RWKV) architecture with feature extraction techniques such as Visual RWKV Spatial Mixing (VRSM) and Visual RWKV Channel Mixing (VRCM), aiming to overcome the limitations of existing methods and achieve superior SISR performance. This work has proved able to provide effective solutions for high-quality image reconstruction. Under the 4x Super-Resolution tasks, compared to the MambaIR model, we achieved an average improvement of 0.26% in PSNR and 0.16% in SSIM.
A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation
Authors: Moein Heidari, Ehsan Khodapanah Aghdam, Alexander Manzella, Daniel Hsu, Rebecca Scalabrino, Wenjin Chen, David J. Foran, Ilker Hacihaliloglu
Date: 2025-02-01 00:00:00
Tasks: Diagnostic, Mamba, Organ Segmentation, Segmentation, Tumor Segmentation

The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to vital structures. Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming. Automatic segmentation using U-Net and its variants, incorporating Vision Transformer (ViT) elements, has shown promising results but struggles with high computational demands. To address this, architectures like the Mamba State Space Model (SSM) and Extended Long-Short Term Memory (xLSTM) offer efficient solutions by handling long-range dependencies with lower resource consumption. This study evaluates U-Net enhancements, including CNN, ViT, Mamba, and xLSTM, on a new in-house CT dataset and a public organ segmentation dataset. The proposed ViLU-Net model integrates Vi-blocks for improved segmentation. Results highlight xLSTM's efficiency in the U-Net framework. The code is publicly accessible on GitHub.
Patch Triplet Similarity Purification for Guided Real-World Low-Dose CT Image Denoising
Authors: Junhao Long, Fengwei Yang, Juncheng Yan, Baoping Zhang, Chao Jin, Jian Yang, Changliang Zou, Jun Xu
Date: 2025-02-01 00:00:00
Tasks: Denoising, Image Denoising, Triplet

Image denoising of low-dose computed tomography (LDCT) is an important problem for clinical diagnosis with reduced radiation exposure. Previous methods are mostly trained with pairs of synthetic or misaligned LDCT and normal-dose CT (NDCT) images. However, trained with synthetic noise or misaligned LDCT/NDCT image pairs, the denoising networks would suffer from blurry structure or motion artifacts. Since non-contrast CT (NCCT) images share the content characteristics to the corresponding NDCT images in a three-phase scan, they can potentially provide useful information for real-world LDCT image denoising. To exploit this aspect, in this paper, we propose to incorporate clean NCCT images as useful guidance for the learning of real-world LDCT image denoising networks. To alleviate the issue of spatial misalignment in training data, we design a new Patch Triplet Similarity Purification (PTSP) strategy to select highly similar patch (instead of image) triplets of LDCT, NDCT, and NCCT images for network training. Furthermore, we modify two image denoising transformers of SwinIR and HAT to accommodate the NCCT image guidance, by replacing vanilla self-attention with cross-attention. On our collected clinical dataset, the modified transformers trained with the data selected by our PTSP strategy show better performance than 15 comparison methods on real-world LDCT image denoising. Ablation studies validate the effectiveness of our NCCT image guidance and PTSP strategy. We will publicly release our data and code.
Learning Difference-of-Convex Regularizers for Inverse Problems: A Flexible Framework with Theoretical Guarantees
Authors: Yasi Zhang, Oscar Leong
Date: 2025-02-01 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

Learning effective regularization is crucial for solving ill-posed inverse problems, which arise in a wide range of scientific and engineering applications. While data-driven methods that parameterize regularizers using deep neural networks have demonstrated strong empirical performance, they often result in highly nonconvex formulations that lack theoretical guarantees. Recent work has shown that incorporating structured nonconvexity into neural network-based regularizers, such as weak convexity, can strike a balance between empirical performance and theoretical tractability. In this paper, we demonstrate that a broader class of nonconvex functions, difference-of-convex (DC) functions, can yield improved empirical performance while retaining strong convergence guarantees. The DC structure enables the use of well-established optimization algorithms, such as the Difference-of-Convex Algorithm (DCA) and a Proximal Subgradient Method (PSM), which extend beyond standard gradient descent. Furthermore, we provide theoretical insights into the conditions under which optimal regularizers can be expressed as DC functions. Extensive experiments on computed tomography (CT) reconstruction tasks show that our approach achieves strong performance across sparse and limited-view settings, consistently outperforming other weakly supervised learned regularizers. Our code is available at \url{https://github.com/YasminZhang/ADCR}.
Improving Quality Control Of MRI Images Using Synthetic Motion Data
Authors: Charles Bricout, Kang Ik K. Cho, Michael Harms, Ofer Pasternak, Carrie E. Bearden, Patrick D. McGorry, Rene S. Kahn, John Kane, Barnaby Nelson, Scott W. Woods, Martha E. Shenton, Sylvain Bouix, Samira Ebrahimi Kahou
Date: 2025-01-31 00:00:00
Tasks: Transfer Learning

MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder the development of reliable automated QC systems. To address these issues, we introduce an approach that pretrains a model on synthetically generated motion artifacts before applying transfer learning for QC classification. This method not only improves the accuracy in identifying poor-quality scans but also reduces training time and resource requirements compared to training from scratch. By leveraging synthetic data, we provide a more robust and resource-efficient solution for QC automation in MRI, paving the way for broader adoption in diverse research settings.