pwc-archive/papers-with-abstracts huggingface dataset:
A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI
Authors: Shuvashis Sarker
Date: 2025-06-08 00:00:00

COVID-19 is a rapidly spreading and highly infectious virus which has triggered a global pandemic, profoundly affecting millions across the world. The pandemic has introduced unprecedented challenges in public health, economic stability, and societal structures, necessitating the implementation of extensive and multifaceted health interventions globally. It had a tremendous impact on Bangladesh by April 2024, with around 29,495 fatalities and more than 2 million confirmed cases. This study focuses on improving COVID-19 detection in CXR images by utilizing a dataset of 4,350 images from Bangladesh categorized into four classes: Normal, Lung-Opacity, COVID-19 and Viral-Pneumonia. ML, DL and TL models are employed with the VGG19 model achieving an impressive 98% accuracy. LIME is used to explain model predictions, highlighting the regions and features influencing classification decisions. SMOTE is applied to address class imbalances. By providing insight into both correct and incorrect classifications, the study emphasizes the importance of XAI in enhancing the transparency and reliability of models, ultimately improving the effectiveness of detection from CXR images.
Transfer Learning and Explainable AI for Brain Tumor Classification: A Study Using MRI Data from Bangladesh
Authors: Shuvashis Sarker
Date: 2025-06-08 00:00:00
Tasks: Brain Tumor Classification, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Transfer Learning

Brain tumors, regardless of being benign or malignant, pose considerable health risks, with malignant tumors being more perilous due to their swift and uncontrolled proliferation, resulting in malignancy. Timely identification is crucial for enhancing patient outcomes, particularly in nations such as Bangladesh, where healthcare infrastructure is constrained. Manual MRI analysis is arduous and susceptible to inaccuracies, rendering it inefficient for prompt diagnosis. This research sought to tackle these problems by creating an automated brain tumor classification system utilizing MRI data obtained from many hospitals in Bangladesh. Advanced deep learning models, including VGG16, VGG19, and ResNet50, were utilized to classify glioma, meningioma, and various brain cancers. Explainable AI (XAI) methodologies, such as Grad-CAM and Grad-CAM++, were employed to improve model interpretability by emphasizing the critical areas in MRI scans that influenced the categorization. VGG16 achieved the most accuracy, attaining 99.17%. The integration of XAI enhanced the system's transparency and stability, rendering it more appropriate for clinical application in resource-limited environments such as Bangladesh. This study highlights the capability of deep learning models, in conjunction with explainable artificial intelligence (XAI), to enhance brain tumor detection and identification in areas with restricted access to advanced medical technologies.
Simultaneous Segmentation of Ventricles and Normal/Abnormal White Matter Hyperintensities in Clinical MRI using Deep Learning
Authors: Mahdi Bashiri Bawil, Mousa Shamsi, Abolhassan Shakeri Bavil
Date: 2025-06-08 00:00:00
Tasks: Computational Efficiency, Segmentation

Multiple sclerosis (MS) diagnosis and monitoring rely heavily on accurate assessment of brain MRI biomarkers, particularly white matter hyperintensities (WMHs) and ventricular changes. Current segmentation approaches suffer from several limitations: they typically segment these structures independently despite their pathophysiological relationship, struggle to differentiate between normal and pathological hyperintensities, and are poorly optimized for anisotropic clinical MRI data. We propose a novel 2D pix2pix-based deep learning framework for simultaneous segmentation of ventricles and WMHs with the unique capability to distinguish between normal periventricular hyperintensities and pathological MS lesions. Our method was developed and validated on FLAIR MRI scans from 300 MS patients. Compared to established methods (SynthSeg, Atlas Matching, BIANCA, LST-LPA, LST-LGA, and WMH-SynthSeg), our approach achieved superior performance for both ventricle segmentation (Dice: 0.801+/-0.025, HD95: 18.46+/-7.1mm) and WMH segmentation (Dice: 0.624+/-0.061, precision: 0.755+/-0.161). Furthermore, our method successfully differentiated between normal and abnormal hyperintensities with a Dice coefficient of 0.647. Notably, our approach demonstrated exceptional computational efficiency, completing end-to-end processing in approximately 4 seconds per case, up to 36 times faster than baseline methods, while maintaining minimal resource requirements. This combination of improved accuracy, clinically relevant differentiation capability, and computational efficiency addresses critical limitations in current neuroimaging analysis, potentially enabling integration into routine clinical workflows and enhancing MS diagnosis and monitoring.
SiliCoN: Simultaneous Nuclei Segmentation and Color Normalization of Histological Images
Authors: Suman Mahapatra, Pradipta Maji
Date: 2025-06-08 00:00:00
Tasks: Color Normalization, Segmentation

Segmentation of nuclei regions from histological images is an important task for automated computer-aided analysis of histological images, particularly in the presence of impermissible color variation in the color appearance of stained tissue images. While color normalization enables better nuclei segmentation, accurate segmentation of nuclei structures makes color normalization rather trivial. In this respect, the paper proposes a novel deep generative model for simultaneously segmenting nuclei structures and normalizing color appearance of stained histological images.This model judiciously integrates the merits of truncated normal distribution and spatial attention. The model assumes that the latent color appearance information, corresponding to a particular histological image, is independent of respective nuclei segmentation map as well as embedding map information. The disentangled representation makes the model generalizable and adaptable as the modification or loss in color appearance information cannot be able to affect the nuclei segmentation map as well as embedding information. Also, for dealing with the stain overlap of associated histochemical reagents, the prior for latent color appearance code is assumed to be a mixture of truncated normal distributions. The proposed model incorporates the concept of spatial attention for segmentation of nuclei regions from histological images. The performance of the proposed approach, along with a comparative analysis with related state-of-the-art algorithms, has been demonstrated on publicly available standard histological image data sets.
Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images
Authors: Suman Mahapatra, Pradipta Maji
Date: 2025-06-08 00:00:00
Tasks: Image-to-Image Translation, Segmentation

Segmentation of nuclei regions from histological images enables morphometric analysis of nuclei structures, which in turn helps in the detection and diagnosis of diseases under consideration. To develop a nuclei segmentation algorithm, applicable to different types of target domain representations, image-to-image translation networks can be considered as they are invariant to target domain image representations. One of the important issues with image-to-image translation models is that they fail miserably when the information content between two image domains are asymmetric in nature. In this regard, the paper introduces a new deep generative model for segmenting nuclei structures from histological images. The proposed model considers an embedding space for handling information-disparity between information-rich histological image space and information-poor segmentation map domain. Integrating judiciously the concepts of optimal transport and measure theory, the model develops an invertible generator, which provides an efficient optimization framework with lower network complexity. The concept of invertible generator automatically eliminates the need of any explicit cycle-consistency loss. The proposed model also introduces a spatially-constrained squeeze operation within the framework of invertible generator to maintain spatial continuity within the image patches. The model provides a better trade-off between network complexity and model performance compared to other existing models having complex network architectures. The performance of the proposed deep generative model, along with a comparison with state-of-the-art nuclei segmentation methods, is demonstrated on publicly available histological image data sets.
Quanta Diffusion
Authors: Prateek Chennuri, Dongdong Fu, Stanley H. Chan
Date: 2025-06-07 00:00:00
Tasks: Motion Estimation, Video Reconstruction

We present Quanta Diffusion (QuDi), a powerful generative video reconstruction method for single-photon imaging. QuDi is an algorithm supporting the latest Quanta Image Sensors (QIS) and Single Photon Avalanche Diodes (SPADs) for extremely low-light imaging conditions. Compared to existing methods, QuDi overcomes the difficulties of simultaneously managing the motion and the strong shot noise. The core innovation of QuDi is to inject a physics-based forward model into the diffusion algorithm, while keeping the motion estimation in the loop. QuDi demonstrates an average of 2.4 dB PSNR improvement over the best existing methods.
NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
Authors: Reese Kneeland, Paul S. Scotti, Ghislain St-Yves, Jesse Breedlove, Kendrick Kay, Thomas Naselaris
Date: 2025-06-07 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Reconstruction

We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired with seen images that enabled unprecedented improvements in fMRI-to-image reconstruction efforts. Recent models trained on NSD have been evaluated only on seen image reconstruction. Using NSD-Imagery, it is possible to assess how well these models perform on mental image reconstruction. This is a challenging generalization requirement because mental images are encoded in human brain activity with relatively lower signal-to-noise and spatial resolution; however, generalization from seen to mental imagery is critical for real-world applications in medical domains and brain-computer interfaces, where the desired information is always internally generated. We provide benchmarks for a suite of recent NSD-trained open-source visual decoding models (MindEye1, MindEye2, Brain Diffuser, iCNN, Takagi et al.) on NSD-Imagery, and show that the performance of decoding methods on mental images is largely decoupled from performance on vision reconstruction. We further demonstrate that architectural choices significantly impact cross-decoding performance: models employing simple linear decoding architectures and multimodal feature decoding generalize better to mental imagery, while complex architectures tend to overfit visual training data. Our findings indicate that mental imagery datasets are critical for the development of practical applications, and establish NSD-Imagery as a useful resource for better aligning visual decoding methods with this goal.
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
Authors: Qianqian Zhao, Chunle Guo, Tianyi Zhang, Junpei Zhang, Peiyang Jia, Tan Su, Wenjie Jiang, Chongyi Li
Date: 2025-06-07 00:00:00
Tasks: Super-Resolution, Video Super-Resolution

Omnidirectional image and video super-resolution is a crucial research topic in low-level vision, playing an essential role in virtual reality and augmented reality applications. Its goal is to reconstruct high-resolution images or video frames from low-resolution inputs, thereby enhancing detail preservation and enabling more accurate scene analysis and interpretation. In recent years, numerous innovative and effective approaches have been proposed, predominantly based on deep learning techniques, involving diverse network architectures, loss functions, projection strategies, and training datasets. This paper presents a systematic review of recent progress in omnidirectional image and video super-resolution, focusing on deep learning-based methods. Given that existing datasets predominantly rely on synthetic degradation and fall short in capturing real-world distortions, we introduce a new dataset, 360Insta, that comprises authentically degraded omnidirectional images and videos collected under diverse conditions, including varying lighting, motion, and exposure settings. This dataset addresses a critical gap in current omnidirectional benchmarks and enables more robust evaluation of the generalization capabilities of omnidirectional super-resolution methods. We conduct comprehensive qualitative and quantitative evaluations of existing methods on both public datasets and our proposed dataset. Furthermore, we provide a systematic overview of the current status of research and discuss promising directions for future exploration. All datasets, methods, and evaluation metrics introduced in this work are publicly available and will be regularly updated. Project page: https://github.com/nqian1/Survey-on-ODISR-and-ODVSR.
Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery
Authors: Shayan shekarforoush, David B. Lindell, Marcus A. Brubaker, David J. Fleet
Date: 2025-06-06 00:00:00
Tasks: 3D Reconstruction, Inductive Bias

Cryo-EM is a transformational paradigm in molecular biology where computational methods are used to infer 3D molecular structure at atomic resolution from extremely noisy 2D electron microscope images. At the forefront of research is how to model the structure when the imaged particles exhibit non-rigid conformational flexibility and compositional variation where parts are sometimes missing. We introduce a novel 3D reconstruction framework with a hierarchical Gaussian mixture model, inspired in part by Gaussian Splatting for 4D scene reconstruction. In particular, the structure of the model is grounded in an initial process that infers a part-based segmentation of the particle, providing essential inductive bias in order to handle both conformational and compositional variability. The framework, called CryoSPIRE, is shown to reveal biologically meaningful structures on complex experimental datasets, and establishes a new state-of-the-art on CryoBench, a benchmark for cryo-EM heterogeneity methods.
Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
Authors: Zofia Rudnicka, Januszcz Szczepanski, Agnieszka Pregowska
Date: 2025-06-06 00:00:00
Tasks: Breast Cancer Detection, Cancer Classification, Deep Learning

Spiking Neural Networks (SNNs) event-driven nature enables efficient encoding of spatial and temporal features, making them suitable for dynamic time-dependent data processing. Despite their biological relevance, SNNs have seen limited application in medical image recognition due to difficulties in matching the performance of conventional deep learning models. To address this, we propose a novel breast cancer classification approach that combines SNNs with Lempel-Ziv Complexity (LZC) a computationally efficient measure of sequence complexity. LZC enhances the interpretability and accuracy of spike-based models by capturing structural patterns in neural activity. Our study explores both biophysical Leaky Integrate-and-Fire (LIF) and probabilistic Levy-Baxter (LB) neuron models under supervised, unsupervised, and hybrid learning regimes. Experiments were conducted on the Breast Cancer Wisconsin dataset using numerical features derived from medical imaging. LB-based models consistently exceeded 90.00% accuracy, while LIF-based models reached over 85.00%. The highest accuracy of 98.25% was achieved using an ANN-to-SNN conversion method applied to both neuron models comparable to traditional deep learning with back-propagation, but at up to 100 times lower computational cost. This hybrid approach merges deep learning performance with the efficiency and plausibility of SNNs, yielding top results at lower computational cost. We hypothesize that the synergy between temporal-coding, spike-sparsity, and LZC-driven complexity analysis enables more-efficient feature extraction. Our findings demonstrate that SNNs combined with LZC offer promising, biologically plausible alternative to conventional neural networks in medical diagnostics, particularly for resource-constrained or real-time systems.
DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research
Authors: Shanawaj S Madarkar, Mahajabeen Madarkar, Madhumitha V, Teli Prakash, Konda Reddy Mopuri, Vinaykumar MV, KVL Sathwika, Adarsh Kasturi, Gandla Dilip Raj, PVN Supranitha, Harsh Udai
Date: 2025-06-06 00:00:00

Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively curated dermatology dataset comprising over 5,450 clinical images from approximately 3,000 patients across outpatient clinics in South India. Each image is annotated by board-certified dermatologists with over 240 distinct diagnoses, structured under a hierarchical, etiology-based taxonomy adapted from Rook's classification. The dataset captures a wide spectrum of dermatologic conditions and tonal variation commonly seen in Indian outpatient care. We benchmark a range of architectures including convolutional models (ResNet, DenseNet, EfficientNet), transformer-based models (ViT, MaxViT, Swin), and Concept Bottleneck Models to establish baseline performance and explore how anatomical and concept-level cues may be integrated. These results are intended to guide future efforts toward interpretable and clinically realistic models. DermaCon-IN provides a scalable and representative foundation for advancing dermatology AI in real-world settings.
LinGuinE: Longitudinal Guidance Estimation for Volumetric Lung Tumour Segmentation
Authors: Nadine Garibli, Mayank Patwari, Bence Csiba, Yi Wei, Kostas Sidiropoulos
Date: 2025-06-06 00:00:00

Segmentation of lung gross tumour volumes is an important first step in radiotherapy and surgical intervention, and is starting to play a role in assessing chemotherapy response. Response to a drug is measured by tracking the tumour volumes over a series of CT scans over a time period i.e. a longitudinal study. However, there currently exist few solutions for automated or semi-automated longitudinal tumour segmentation. This paper introduces LinGuinE, an automated method to segment a longitudinal series of lung tumours. A radiologist must provide an initial input, indicating the location of the tumour in a CT scan at an arbitrary time point. LinGuinE samples points inside this tumour and propagates them to another time point using rigid registration. A click validity classifier selects points which still fall within the tumour; these are used to automatically create a segmentation in the new time point. We test LinGuinE on a dataset acquired from a phase 3 clinical trial for lung tumours and the publicly available 4-D lung CBCT dataset. We find that LinGuinE improves the Dice on both test sets by over 20% (p< 0.05) across 63 longitudinal studies. We show that any time point can be used as a starting point, conduct ablation experiments, and find that our LinGuinE setup yields the best results on both test datasets.
FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound
Authors: Minglang Chen, Jie He, Caixu Xu, Bocheng Liang, Shengli Li, Guannan He, Xiongjie Tao
Date: 2025-06-06 00:00:00
Tasks: image-classification, Image Classification

ResNet has been widely used in image classification tasks due to its ability to model the residual dependence of constant mappings for linear computation. However, the ResNet method adopts a unidirectional transfer of features and lacks an effective method to correlate contextual information, which is not effective in classifying fetal ultrasound images in the classification task, and fetal ultrasound images have problems such as low contrast, high similarity, and high noise. Therefore, we propose a bilateral multi-scale information fusion network-based FPDANet to address the above challenges. Specifically, we design the positional attention mechanism (DAN) module, which utilizes the similarity of features to establish the dependency of different spatial positional features and enhance the feature representation. In addition, we design a bilateral multi-scale (FPAN) information fusion module to capture contextual and global feature dependencies at different feature scales, thereby further improving the model representation. FPDANet classification results obtained 91.05\% and 100\% in Top-1 and Top-5 metrics, respectively, and the experimental results proved the effectiveness and robustness of FPDANet.
Implicit Neural Representation-Based MRI Reconstruction Method with Sensitivity Map Constraints
Authors: Lixuan Rao, Xinlin Zhang, Yiman Huang, Tao Tan, Tong Tong
Date: 2025-06-06 00:00:00
Tasks: Diagnostic, MRI Reconstruction, Sensitivity

Magnetic Resonance Imaging (MRI) is a widely utilized diagnostic tool in clinical settings, but its application is limited by the relatively long acquisition time. As a result, fast MRI reconstruction has become a significant area of research. In recent years, Implicit Neural Representation (INR), as a scan-specific method, has demonstrated outstanding performance in fast MRI reconstruction without fully-sampled images for training. High acceleration reconstruction poses a challenging problem, and a key component in achieving high-quality reconstruction with much few data is the accurate estimation of coil sensitivity maps. However, most INR-based methods apply regularization constraints solely to the generated images, while overlooking the characteristics of the coil sensitivity maps. To handle this, this work proposes a joint coil sensitivity map and image estimation network, termed INR-CRISTAL. The proposed INR-CRISTAL introduces an extra sensitivity map regularization in the INR networks to make use of the smooth characteristics of the sensitivity maps. Experimental results show that INR-CRISTAL provides more accurate coil sensitivity estimates with fewer artifacts, and delivers superior reconstruction performance in terms of artifact removal and structure preservation. Moreover, INR-CRISTAL demonstrates stronger robustness to automatic calibration signals and the acceleration rate compared to existing methods.