pwc-archive/papers-with-abstracts huggingface dataset:
AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settings
Authors: Bargava Subramanian, Naveen Kumarasami, Praveen Shastry, Raghotham Sripadraj, Kalyan Sivasailam, Anandakumar D, Abinaya Ramachandran, Sudhir MP, Gunakutti G, Kishore Prasath Venkatesh
Date: 2025-03-26 00:00:00
Tasks: Diagnostic

Study Design: This study presents the development of an autonomous AI system for MRI spine pathology detection, trained on a dataset of 2 million MRI spine scans sourced from diverse healthcare facilities across India. The AI system integrates advanced architectures, including Vision Transformers, U-Net with cross-attention, MedSAM, and Cascade R-CNN, enabling comprehensive classification, segmentation, and detection of 43 distinct spinal pathologies. The dataset is balanced across age groups, genders, and scanner manufacturers to ensure robustness and adaptability. Subgroup analyses were conducted to validate the model's performance across different patient demographics, imaging conditions, and equipment types. Performance: The AI system achieved up to 97.9 percent multi-pathology detection, demonstrating consistent performance across age, gender, and manufacturer subgroups. The normal vs. abnormal classification achieved 98.0 percent accuracy, and the system was deployed across 13 major healthcare enterprises in India, encompassing diagnostic centers, large hospitals, and government facilities. During deployment, it processed approximately 100,000 plus MRI spine scans, leading to reduced reporting times and increased diagnostic efficiency by automating the identification of common spinal conditions. Conclusion: The AI system's high precision and recall validate its capability as a reliable tool for autonomous normal/abnormal classification, pathology segmentation, and detection. Its scalability and adaptability address critical diagnostic gaps, optimize radiology workflows, and improve patient care across varied healthcare environments in India.
3D Convolutional Neural Networks for Improved Detection of Intracranial bleeding in CT Imaging
Authors: Bargava Subramanian, Naveen Kumarasami, Praveen Shastry, Kalyan Sivasailam, Anandakumar D, Elakkiya R, Harsha KG, Rithanya V, Harini T, Afshin Hussain, Kishore Prasath Venkatesh
Date: 2025-03-26 00:00:00
Tasks: Diagnostic

Background: Intracranial bleeding (IB) is a life-threatening condition caused by traumatic brain injuries, including epidural, subdural, subarachnoid, and intraparenchymal hemorrhages. Rapid and accurate detection is crucial to prevent severe complications. Traditional imaging can be slow and prone to variability, especially in high-pressure scenarios. Artificial Intelligence (AI) provides a solution by quickly analyzing medical images, identifying subtle hemorrhages, and flagging urgent cases. By enhancing diagnostic speed and accuracy, AI improves workflows and patient care. This article explores AI's role in transforming IB detection in emergency settings. Methods: A U-shaped 3D Convolutional Neural Network (CNN) automates IB detection and classification in volumetric CT scans. Advanced preprocessing, including CLAHE and intensity normalization, enhances image quality. The architecture preserves spatial and contextual details for precise segmentation. A dataset of 2,912 annotated CT scans was used for training and evaluation. Results: The model achieved high performance across major bleed types, with precision, recall, and accuracy exceeding 90 percent in most cases 96 percent precision for epidural hemorrhages and 94 percent accuracy for subarachnoid hemorrhages. Its ability to classify and localize hemorrhages highlights its clinical reliability. Conclusion: This U-shaped 3D CNN offers a scalable solution for automating IB detection, reducing diagnostic delays, and improving emergency care outcomes. Future work will expand dataset diversity, optimize real-time processing, and integrate multimodal data for enhanced clinical applicability.
Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack
Authors: M. Kerem Aydin, Yi-Chun Hung, Jaclyn Pytlarz, Qi Guo, Emma Alexander
Date: 2025-03-26 00:00:00
Tasks: Compressive Sensing

Hyperspectral cameras face harsh trade-offs between spatial, spectral, and temporal resolution in an inherently low-photon regime. Computational imaging systems break through these trade-offs with compressive sensing, but require complex optics and/or extensive compute. We present Spectrum from Defocus (SfD), a chromatic focal sweep method that recovers state-of-the-art hyperspectral images with a small system of off-the-shelf optics and < 1 second of compute. Our camera uses two lenses and a grayscale sensor to preserve nearly all incident light in a chromatically-aberrated focal stack. Our physics-based iterative algorithm efficiently demixes, deconvolves, and denoises the blurry grayscale focal stack into a sharp spectral image. The combination of photon efficiency, optical simplicity, and physical modeling makes SfD a promising solution for fast, compact, interpretable hyperspectral imaging.
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis
Authors: Yu Xin, Gorkem Can Ates, Kuang Gong, Wei Shao
Date: 2025-03-25 00:00:00
Tasks: Contrastive Learning, Image-text Retrieval, Language Modeling, Language Modelling, Medical Image Analysis, Question Answering, Text Retrieval, Visual Question Answering, Visual Question Answering (VQA)

Vision-language models (VLMs) have shown promise in 2D medical image analysis, but extending them to 3D remains challenging due to the high computational demands of volumetric data and the difficulty of aligning 3D spatial features with clinical text. We present Med3DVLM, a 3D VLM designed to address these challenges through three key innovations: (1) DCFormer, an efficient encoder that uses decomposed 3D convolutions to capture fine-grained spatial features at scale; (2) SigLIP, a contrastive learning strategy with pairwise sigmoid loss that improves image-text alignment without relying on large negative batches; and (3) a dual-stream MLP-Mixer projector that fuses low- and high-level image features with text embeddings for richer multi-modal representations. We evaluate our model on the M3D dataset, which includes radiology reports and VQA data for 120,084 3D medical images. Results show that Med3DVLM achieves superior performance across multiple benchmarks. For image-text retrieval, it reaches 61.00% R@1 on 2,000 samples, significantly outperforming the current state-of-the-art M3D model (19.10%). For report generation, it achieves a METEOR score of 36.42% (vs. 14.38%). In open-ended visual question answering (VQA), it scores 36.76% METEOR (vs. 33.58%), and in closed-ended VQA, it achieves 79.95% accuracy (vs. 75.78%). These results highlight Med3DVLM's ability to bridge the gap between 3D imaging and language, enabling scalable, multi-task reasoning across clinical applications. Our code is publicly available at https://github.com/mirthAI/Med3DVLM.
Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment
Authors: Junzhi Ning, Dominic Marshall, Yijian Gao, Xiaodan Xing Yang Nan, Yingying Fang, Sheng Zhang, Matthieu Komorowski, Guang Yang
Date: 2025-03-25 00:00:00
Tasks: Lesion Classification, Sensitivity, Translation

Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases. However, lung opac- ities in CXRs frequently obscure anatomical structures, impeding clear identification of lung borders and complicating the localization of pathology. This challenge significantly hampers segmentation accuracy and precise lesion identification, which are crucial for diagnosis. To tackle these issues, our study proposes an unpaired CXR translation framework that converts CXRs with lung opacities into counterparts without lung opacities while preserving semantic features. Central to our approach is the use of adaptive activation masks to selectively modify opacity regions in lung CXRs. Cross-domain alignment ensures translated CXRs without opacity issues align with feature maps and prediction labels from a pre-trained CXR lesion classifier, facilitating the interpretability of the translation process. We validate our method using RSNA, MIMIC-CXR-JPG and JSRT datasets, demonstrating superior translation quality through lower Frechet Inception Distance (FID) and Kernel Inception Distance (KID) scores compared to existing meth- ods (FID: 67.18 vs. 210.4, KID: 0.01604 vs. 0.225). Evaluation on RSNA opacity, MIMIC acute respiratory distress syndrome (ARDS) patient CXRs and JSRT CXRs show our method enhances segmentation accuracy of lung borders and improves lesion classification, further underscoring its potential in clinical settings (RSNA: mIoU: 76.58% vs. 62.58%, Sensitivity: 85.58% vs. 77.03%; MIMIC ARDS: mIoU: 86.20% vs. 72.07%, Sensitivity: 92.68% vs. 86.85%; JSRT: mIoU: 91.08% vs. 85.6%, Sensitivity: 97.62% vs. 95.04%). Our approach advances CXR imaging analysis, especially in investigating segmentation impacts through image translation techniques.
Unpaired Object-Level SAR-to-Optical Image Translation for Aircraft with Keypoints-Guided Diffusion Models
Authors: Ruixi You, Hecheng Jia, Feng Xu
Date: 2025-03-25 00:00:00
Tasks: Translation, Zero-shot Generalization

Synthetic Aperture Radar (SAR) imagery provides all-weather, all-day, and high-resolution imaging capabilities but its unique imaging mechanism makes interpretation heavily reliant on expert knowledge, limiting interpretability, especially in complex target tasks. Translating SAR images into optical images is a promising solution to enhance interpretation and support downstream tasks. Most existing research focuses on scene-level translation, with limited work on object-level translation due to the scarcity of paired data and the challenge of accurately preserving contour and texture details. To address these issues, this study proposes a keypoint-guided diffusion model (KeypointDiff) for SAR-to-optical image translation of unpaired aircraft targets. This framework introduces supervision on target class and azimuth angle via keypoints, along with a training strategy for unpaired data. Based on the classifier-free guidance diffusion architecture, a class-angle guidance module (CAGM) is designed to integrate class and angle information into the diffusion generation process. Furthermore, adversarial loss and consistency loss are employed to improve image fidelity and detail quality, tailored for aircraft targets. During sampling, aided by a pre-trained keypoint detector, the model eliminates the requirement for manually labeled class and azimuth information, enabling automated SAR-to-optical translation. Experimental results demonstrate that the proposed method outperforms existing approaches across multiple metrics, providing an efficient and effective solution for object-level SAR-to-optical translation and downstream tasks. Moreover, the method exhibits strong zero-shot generalization to untrained aircraft types with the assistance of the keypoint detector.
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges
Authors: Valerii A. Zuev, Elena G. Salmagambetova, Stepan N. Djakov, Lev V. Utkin
Date: 2025-03-25 00:00:00
Tasks: Diversity, EEG, Seizure Detection

Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitating automated tools for seizure detection. Recent advancements in machine learning have shown promise in real-time seizure detection and prediction using EEG and video data. However, diversity of seizure symptoms, markup ambiguities, and limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data. We also propose a novel pipeline for treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain.
GRN+: A Simplified Generative Reinforcement Network for Tissue Layer Analysis in 3D Ultrasound Images for Chronic Low-back Pain
Authors: Zixue Zeng, Xiaoyan Zhao, Matthew Cartier, Xin Meng, Jiantao Pu
Date: 2025-03-25 00:00:00
Tasks: Segmentation

3D ultrasound delivers high-resolution, real-time images of soft tissues, which is essential for pain research. However, manually distinguishing various tissues for quantitative analysis is labor-intensive. To streamline this process, we developed and validated GRN+, a novel multi-model framework that automates layer segmentation with minimal annotated data. GRN+ combines a ResNet-based generator and a U-Net segmentation model. Through a method called Segmentation-guided Enhancement (SGE), the generator produces new images and matching masks under the guidance of the segmentation model, with its weights adjusted according to the segmentation loss gradient. To prevent gradient explosion and secure stable training, a two-stage backpropagation strategy was implemented: the first stage propagates the segmentation loss through both the generator and segmentation model, while the second stage concentrates on optimizing the segmentation model alone, thereby refining mask prediction using the generated images. Tested on 69 fully annotated 3D ultrasound scans from 29 subjects with six manually labeled tissue layers, GRN+ outperformed all other semi-supervised methods in terms of the Dice coefficient using only 5% labeled data, despite not using unlabeled data for unsupervised training. Additionally, when applied to fully annotated datasets, GRN+ with SGE achieved a 2.16% higher Dice coefficient while incurring lower computational costs compared to other models. Overall, GRN+ provides accurate tissue segmentation while reducing both computational expenses and the dependency on extensive annotations, making it an effective tool for 3D ultrasound analysis in cLBP patients.
InterSliceBoost: Identifying Tissue Layers in Three-dimensional Ultrasound Images for Chronic Lower Back Pain (cLBP) Assessment
Authors: Zixue Zeng, Matthew Cartier, Xiaoyan Zhao, PengYu Chen, Xin Meng, Zhiyu Sheng, Maryam Satarpour, John M Cormack, Allison C. Bean, Ryan P. Nussbaum, Maya Maurer, Emily Landis-Walkenhorst, Kang Kim, Ajay D. Wasan, Jiantao Pu
Date: 2025-03-25 00:00:00
Tasks: Segmentation

Available studies on chronic lower back pain (cLBP) typically focus on one or a few specific tissues rather than conducting a comprehensive layer-by-layer analysis. Since three-dimensional (3-D) images often contain hundreds of slices, manual annotation of these anatomical structures is both time-consuming and error-prone. We aim to develop and validate a novel approach called InterSliceBoost to enable the training of a segmentation model on a partially annotated dataset without compromising segmentation performance. The architecture of InterSliceBoost includes two components: an inter-slice generator and a segmentation model. The generator utilizes residual block-based encoders to extract features from adjacent image-mask pairs (IMPs). Differential features are calculated and input into a decoder to generate inter-slice IMPs. The segmentation model is trained on partially annotated datasets (e.g., skipping 1, 2, 3, or 7 images) and the generated inter-slice IMPs. To validate the performance of InterSliceBoost, we utilized a dataset of 76 B-mode ultrasound scans acquired on 29 subjects enrolled in an ongoing cLBP study. InterSliceBoost, trained on only 33% of the image slices, achieved a mean Dice coefficient of 80.84% across all six layers on the independent test set, with Dice coefficients of 73.48%, 61.11%, 81.87%, 95.74%, 83.52% and 88.74% for segmenting dermis, superficial fat, superficial fascial membrane, deep fat, deep fascial membrane, and muscle. This performance is significantly higher than the conventional model trained on fully annotated images (p<0.05). InterSliceBoost can effectively segment the six tissue layers depicted on 3-D B-model ultrasound images in settings with partial annotations.
MindfulLIME: A Stable Solution for Explanations of Machine Learning Models with Enhanced Localization Precision -- A Medical Image Case Study
Authors: Shakiba Rahimiaghdam, Hande Alemdar
Date: 2025-03-25 00:00:00

Ensuring transparency in machine learning decisions is critically important, especially in sensitive sectors such as healthcare, finance, and justice. Despite this, some popular explainable algorithms, such as Local Interpretable Model-agnostic Explanations (LIME), often produce unstable explanations due to the random generation of perturbed samples. Random perturbation introduces small changes or noise to modified instances of the original data, leading to inconsistent explanations. Even slight variations in the generated samples significantly affect the explanations provided by such models, undermining trust and hindering the adoption of interpretable models. To address this challenge, we propose MindfulLIME, a novel algorithm that intelligently generates purposive samples using a graph-based pruning algorithm and uncertainty sampling. MindfulLIME substantially improves the consistency of visual explanations compared to random sampling approaches. Our experimental evaluation, conducted on a widely recognized chest X-ray dataset, confirms MindfulLIME's stability with a 100% success rate in delivering reliable explanations under identical conditions. Additionally, MindfulLIME improves the localization precision of visual explanations by reducing the distance between the generated explanations and the actual local annotations compared to LIME. We also performed comprehensive experiments considering various segmentation algorithms and sample numbers, focusing on stability, quality, and efficiency. The results demonstrate the outstanding performance of MindfulLIME across different segmentation settings, generating fewer high-quality samples within a reasonable processing time. By addressing the stability limitations of LIME in image data, MindfulLIME enhances the trustworthiness and interpretability of machine learning models in specific medical imaging applications, a critical domain.
High-Quality Spatial Reconstruction and Orthoimage Generation Using Efficient 2D Gaussian Splatting
Authors: Qian Wang, Zhihao Zhan, Jialei He, Zhituo Tu, Xiang Zhu, Jie Yuan
Date: 2025-03-25 00:00:00
Tasks: Decision Making

Highly accurate geometric precision and dense image features characterize True Digital Orthophoto Maps (TDOMs), which are in great demand for applications such as urban planning, infrastructure management, and environmental monitoring.Traditional TDOM generation methods need sophisticated processes, such as Digital Surface Models (DSM) and occlusion detection, which are computationally expensive and prone to errors.This work presents an alternative technique rooted in 2D Gaussian Splatting (2DGS), free of explicit DSM and occlusion detection. With depth map generation, spatial information for every pixel within the TDOM is retrieved and can reconstruct the scene with high precision. Divide-and-conquer strategy achieves excellent GS training and rendering with high-resolution TDOMs at a lower resource cost, which preserves higher quality of rendering on complex terrain and thin structure without a decrease in efficiency. Experimental results demonstrate the efficiency of large-scale scene reconstruction and high-precision terrain modeling. This approach provides accurate spatial data, which assists users in better planning and decision-making based on maps.
Single Shot AI-assisted quantification of KI-67 proliferation index in breast cancer
Authors: Deepti Madurai Muthu, Priyanka S, Lalitha Rani N, P. G. Kubendran Amos
Date: 2025-03-25 00:00:00
Tasks: Diagnostic, object-detection, Object Detection

Reliable quantification of Ki-67, a key proliferation marker in breast cancer, is essential for molecular subtyping and informed treatment planning. Conventional approaches, including visual estimation and manual counting, suffer from interobserver variability and limited reproducibility. This study introduces an AI-assisted method using the YOLOv8 object detection framework for automated Ki-67 scoring. High-resolution digital images (40x magnification) of immunohistochemically stained tumor sections were captured from Ki-67 hotspot regions and manually annotated by a domain expert to distinguish Ki-67-positive and negative tumor cells. The dataset was augmented and divided into training (80%), validation (10%), and testing (10%) subsets. Among the YOLOv8 variants tested, the Medium model achieved the highest performance, with a mean Average Precision at 50% Intersection over Union (mAP50) exceeding 85% for Ki-67-positive cells. The proposed approach offers an efficient, scalable, and objective alternative to conventional scoring methods, supporting greater consistency in Ki-67 evaluation. Future directions include developing user-friendly clinical interfaces and expanding to multi-institutional datasets to enhance generalizability and facilitate broader adoption in diagnostic practice.
GIViC: Generative Implicit Video Compression
Authors: Ge Gao, Siyue Teng, Tianhao Peng, Fan Zhang, David Bull
Date: 2025-03-25 00:00:00
Tasks: Video Compression

While video compression based on implicit neural representations (INRs) has recently demonstrated great potential, existing INR-based video codecs still cannot achieve state-of-the-art (SOTA) performance compared to their conventional or autoencoder-based counterparts given the same coding configuration. In this context, we propose a Generative Implicit Video Compression framework, GIViC, aiming at advancing the performance limits of this type of coding methods. GIViC is inspired by the characteristics that INRs share with large language and diffusion models in exploiting long-term dependencies. Through the newly designed implicit diffusion process, GIViC performs diffusive sampling across coarse-to-fine spatiotemporal decompositions, gradually progressing from coarser-grained full-sequence diffusion to finer-grained per-token diffusion. A novel Hierarchical Gated Linear Attention-based transformer (HGLA), is also integrated into the framework, which dual-factorizes global dependency modeling along scale and sequential axes. The proposed GIViC model has been benchmarked against SOTA conventional and neural codecs using a Random Access (RA) configuration (YUV 4:2:0, GOPSize=32), and yields BD-rate savings of 15.94%, 22.46% and 8.52% over VVC VTM, DCVC-FM and NVRC, respectively. As far as we are aware, GIViC is the first INR-based video codec that outperforms VTM based on the RA coding configuration. The source code will be made available.