pwc-archive/papers-with-abstracts huggingface dataset:
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging
Authors: Ahmad Hussein, Mukesh Prasad, Ali Anaissi, Ali Braytee
Date: 2025-04-07 00:00:00
Tasks: Prognosis, Survival Analysis

Cancer remains one of the leading causes of mortality worldwide, necessitating accurate diagnosis and prognosis. Whole Slide Imaging (WSI) has become an integral part of clinical workflows with advancements in digital pathology. While various studies have utilized WSIs, their extracted features may not fully capture the most relevant pathological information, and their lack of interpretability limits clinical adoption. In this paper, we propose PATH-X, a framework that integrates Vision Transformers (ViT) and Autoencoders with SHAP (Shapley Additive Explanations) to enhance model explainability for patient stratification and risk prediction using WSIs from The Cancer Genome Atlas (TCGA). A representative image slice is selected from each WSI, and numerical feature embeddings are extracted using Google's pre-trained ViT. These features are then compressed via an autoencoder and used for unsupervised clustering and classification tasks. Kaplan-Meier survival analysis is applied to evaluate stratification into two and three risk groups. SHAP is used to identify key contributing features, which are mapped onto histopathological slices to provide spatial context. PATH-X demonstrates strong performance in breast and glioma cancers, where a sufficient number of WSIs enabled robust stratification. However, performance in lung cancer was limited due to data availability, emphasizing the need for larger datasets to enhance model reliability and clinical applicability.
CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images
Authors: Mingyang Zhu, Yuqiu Liang, Jiacheng Wang
Date: 2025-04-08 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Chronic kidney disease (CKD) is a growing global health concern, necessitating precise and efficient image analysis to aid diagnosis and treatment planning. Automated segmentation of kidney pathology images plays a central role in facilitating clinical workflows, yet conventional segmentation models often require delicate threshold tuning. This paper proposes a novel \textit{Cascaded Threshold-Integrated U-Net (CTI-Unet)} to overcome the limitations of single-threshold segmentation. By sequentially integrating multiple thresholded outputs, our approach can reconcile noise suppression with the preservation of finer structural details. Experiments on the challenging KPIs2024 dataset demonstrate that CTI-Unet outperforms state-of-the-art architectures such as nnU-Net, Swin-Unet, and CE-Net, offering a robust and flexible framework for kidney pathology image segmentation.
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Authors: Jungkyu Park, Jan Witowski, Yanqi Xu, Hari Trivedi, Judy Gichoya, Beatrice Brown-Mulry, Malte Westerhoff, Linda Moy, Laura Heacock, Alana Lewin, Krzysztof J. Geras
Date: 2025-04-08 00:00:00
Tasks: Breast Cancer Detection, Diagnostic

Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer screening. We developed a multi-modal artificial intelligence system integrating FFDM, synthetic mammography, and DBT to provide breast-level predictions and bounding-box localizations of suspicious findings. Our AI system, trained on approximately 500,000 mammography exams, achieved 0.945 AUROC on an internal test set. It demonstrated capacity to reduce recalls by 31.7% and radiologist workload by 43.8% while maintaining 100% sensitivity, underscoring its potential to improve clinical workflows. External validation confirmed strong generalizability, reducing the gap to a perfect AUROC by 35.31%-69.14% relative to strong baselines. In prospective deployment across 18 sites, the system reduced recall rates for low-risk cases. An improved version, trained on over 750,000 exams with additional labels, further reduced the gap by 18.86%-56.62% across large external datasets. Overall, these results underscore the importance of utilizing all available imaging modalities, demonstrate the potential for clinical impact, and indicate feasibility of further reduction of the test error with increased training set when using large-capacity neural networks.
Inter-event Interval Microscopy for Event Cameras
Authors: Changqing Su, Yanqin Chen, Zihan Lin, Zhen Cheng, You Zhou, Bo Xiong, Zhaofei Yu, Tiejun Huang
Date: 2025-04-07 00:00:00

Event cameras, an innovative bio-inspired sensor, differ from traditional cameras by sensing changes in intensity rather than directly perceiving intensity and recording these variations as a continuous stream of "events". The intensity reconstruction from these sparse events has long been a challenging problem. Previous approaches mainly focused on transforming motion-induced events into videos or achieving intensity imaging for static scenes by integrating modulation devices at the event camera acquisition end. In this paper, for the first time, we achieve event-to-intensity conversion using a static event camera for both static and dynamic scenes in fluorescence microscopy. Unlike conventional methods that primarily rely on event integration, the proposed Inter-event Interval Microscopy (IEIM) quantifies the time interval between consecutive events at each pixel. With a fixed threshold in the event camera, the time interval can precisely represent the intensity. At the hardware level, the proposed IEIM integrates a pulse light modulation device within a microscope equipped with an event camera, termed Pulse Modulation-based Event-driven Fluorescence Microscopy. Additionally, we have collected IEIMat dataset under various scenes including high dynamic range and high-speed scenarios. Experimental results on the IEIMat dataset demonstrate that the proposed IEIM achieves superior spatial and temporal resolution, as well as a higher dynamic range, with lower bandwidth compared to other methods. The code and the IEIMat dataset will be made publicly available.
Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
Authors: Peter D. Erickson, Tejas Sudharshan Mathai, Ronald M. Summers
Date: 2025-04-08 00:00:00
Tasks: Lesion Detection, TAG

Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion detection algorithms have been developed with a large public dataset called DeepLesion (32,735 lesions, 32,120 CT slices, 10,594 studies, 4,427 patients, 8 body part labels). However, this dataset contains missing measurements and lesion tags, and exhibits a severe imbalance in the number of lesions per label category. In this work, we utilize a limited subset of DeepLesion (6\%, 1331 lesions, 1309 slices) containing lesion annotations and body part label tags to train a VFNet model to detect lesions and tag them. We address the class imbalance by conducting three experiments: 1) Balancing data by the body part labels, 2) Balancing data by the number of lesions per patient, and 3) Balancing data by the lesion size. In contrast to a randomly sampled (unbalanced) data subset, our results indicated that balancing the body part labels always increased sensitivity for lesions >= 1cm for classes with low data quantities (Bone: 80\% vs. 46\%, Kidney: 77\% vs. 61\%, Soft Tissue: 70\% vs. 60\%, Pelvis: 83\% vs. 76\%). Similar trends were seen for three other models tested (FasterRCNN, RetinaNet, FoveaBox). Balancing data by lesion size also helped the VFNet model improve recalls for all classes in contrast to an unbalanced dataset. We also provide a structured reporting guideline for a ``Lesions'' subsection to be entered into the ``Findings'' section of a radiology report. To our knowledge, we are the first to report the class imbalance in DeepLesion, and have taken data-driven steps to address it in the context of joint lesion detection and tagging.
A Novel Approach to Linking Histology Images with DNA Methylation
Authors: Manahil Raza, Muhammad Dawood, Talha Qaiser, Nasir M. Rajpoot
Date: 2025-04-07 00:00:00
Tasks: Graph Neural Network, Weakly-supervised Learning, whole slide images

DNA methylation is an epigenetic mechanism that regulates gene expression by adding methyl groups to DNA. Abnormal methylation patterns can disrupt gene expression and have been linked to cancer development. To quantify DNA methylation, specialized assays are typically used. However, these assays are often costly and have lengthy processing times, which limits their widespread availability in routine clinical practice. In contrast, whole slide images (WSIs) for the majority of cancer patients can be more readily available. As such, given the ready availability of WSIs, there is a compelling need to explore the potential relationship between WSIs and DNA methylation patterns. To address this, we propose an end-to-end graph neural network based weakly supervised learning framework to predict the methylation state of gene groups exhibiting coherent patterns across samples. Using data from three cohorts from The Cancer Genome Atlas (TCGA) - TCGA-LGG (Brain Lower Grade Glioma), TCGA-GBM (Glioblastoma Multiforme) ($n$=729) and TCGA-KIRC (Kidney Renal Clear Cell Carcinoma) ($n$=511) - we demonstrate that the proposed approach achieves significantly higher AUROC scores than the state-of-the-art (SOTA) methods, by more than $20\%$. We conduct gene set enrichment analyses on the gene groups and show that majority of the gene groups are significantly enriched in important hallmarks and pathways. We also generate spatially enriched heatmaps to further investigate links between histological patterns and DNA methylation states. To the best of our knowledge, this is the first study that explores association of spatially resolved histological patterns with gene group methylation states across multiple cancer types using weakly supervised deep learning.
An ensemble deep learning approach to detect tumors on Mohs micrographic surgery slides
Authors: Abdurrahim Yilmaz, Serra Atilla Aydin, Deniz Temur, Furkan Yuceyalcin, Berkin Deniz Kahya, Rahmetullah Varol, Ozay Gokoz, Gulsum Gencoglan, Huseyin Uvet, Gonca Elcin
Date: 2025-04-07 00:00:00
Tasks: Deep Learning, whole slide images

Mohs micrographic surgery (MMS) is the gold standard technique for removing high risk nonmelanoma skin cancer however, intraoperative histopathological examination demands significant time, effort, and professionality. The objective of this study is to develop a deep learning model to detect basal cell carcinoma (BCC) and artifacts on Mohs slides. A total of 731 Mohs slides from 51 patients with BCCs were used in this study, with 91 containing tumor and 640 without tumor which was defined as non-tumor. The dataset was employed to train U-Net based models that segment tumor and non-tumor regions on the slides. The segmented patches were classified as tumor, or non-tumor to produce predictions for whole slide images (WSIs). For the segmentation phase, the deep learning model success was measured using a Dice score with 0.70 and 0.67 value, area under the curve (AUC) score with 0.98 and 0.96 for tumor and non-tumor, respectively. For the tumor classification, an AUC of 0.98 for patch-based detection, and AUC of 0.91 for slide-based detection was obtained on the test dataset. We present an AI system that can detect tumors and non-tumors in Mohs slides with high success. Deep learning can aid Mohs surgeons and dermatopathologists in making more accurate decisions.
Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
Authors: Tejas Sudharshan Mathai, SungWon Lee, Thomas C. Shen, Zhiyong Lu, Ronald M. Summers
Date: 2025-04-07 00:00:00
Tasks: Sensitivity

Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distinguish benign from malignant nodes, which would require subsequent cancer staging. Sizing is a cumbersome task compounded by the diverse appearances of LNs in mpMRI, which renders their measurement difficult. Furthermore, smaller and potentially metastatic LNs could be missed during a busy clinical day. To alleviate these imaging and workflow problems, we propose a pipeline to universally detect both benign and metastatic nodes in the body for their ensuing measurement. The recently proposed VFNet neural network was employed to identify LN in T2 fat suppressed and diffusion weighted imaging (DWI) sequences acquired by various scanners with a variety of exam protocols. We also use a selective augmentation technique known as Intra-Label LISA (ILL) to diversify the input data samples the model sees during training, such that it improves its robustness during the evaluation phase. We achieved a sensitivity of $\sim$83\% with ILL vs. $\sim$80\% without ILL at 4 FP/vol. Compared with current LN detection approaches evaluated on mpMRI, we show a sensitivity improvement of $\sim$9\% at 4 FP/vol.
Going beyond explainability in multi-modal stroke outcome prediction models
Authors: Jonas Brändli, Maurice Schneeberger, Lisa Herzog, Loran Avci, Nordin Dari, Martin Häansel, Hakim Baazaoui, Pascal Bühler, Susanne Wegener, Beate Sick
Date: 2025-04-07 00:00:00
Tasks: Prediction

Aim: This study aims to enhance interpretability and explainability of multi-modal prediction models integrating imaging and tabular patient data. Methods: We adapt the xAI methods Grad-CAM and Occlusion to multi-modal, partly interpretable deep transformation models (dTMs). DTMs combine statistical and deep learning approaches to simultaneously achieve state-of-the-art prediction performance and interpretable parameter estimates, such as odds ratios for tabular features. Based on brain imaging and tabular data from 407 stroke patients, we trained dTMs to predict functional outcome three months after stroke. We evaluated the models using different discriminatory metrics. The adapted xAI methods were used to generated explanation maps for identification of relevant image features and error analysis. Results: The dTMs achieve state-of-the-art prediction performance, with area under the curve (AUC) values close to 0.8. The most important tabular predictors of functional outcome are functional independence before stroke and NIHSS on admission, a neurological score indicating stroke severity. Explanation maps calculated from brain imaging dTMs for functional outcome highlighted critical brain regions such as the frontal lobe, which is known to be linked to age which in turn increases the risk for unfavorable outcomes. Similarity plots of the explanation maps revealed distinct patterns which give insight into stroke pathophysiology, support developing novel predictors of stroke outcome and enable to identify false predictions. Conclusion: By adapting methods for explanation maps to dTMs, we enhanced the explainability of multi-modal and partly interpretable prediction models. The resulting explanation maps facilitate error analysis and support hypothesis generation regarding the significance of specific image regions in outcome prediction.
Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors
Authors: Nataliia Molchanova, Pedro M. Gordaliza, Alessandro Cagol, Mario Ocampo--Pineda, Po--Jui Lu, Matthias Weigel, Xinjie Chen, Erin S. Beck, Haris Tsagkas, Daniel Reich, Anna Stölting, Pietro Maggi, Delphine Ribes, Adrien Depeursinge, Cristina Granziera, Henning Müller, Meritxell Bach Cuadra
Date: 2025-04-07 00:00:00
Tasks: Image Segmentation, Informativeness, Lesion Segmentation, Medical Image Segmentation, Semantic Segmentation, Uncertainty Quantification

Trustworthy artificial intelligence (AI) is essential in healthcare, particularly for high-stakes tasks like medical image segmentation. Explainable AI and uncertainty quantification significantly enhance AI reliability by addressing key attributes such as robustness, usability, and explainability. Despite extensive technical advances in uncertainty quantification for medical imaging, understanding the clinical informativeness and interpretability of uncertainty remains limited. This study introduces a novel framework to explain the potential sources of predictive uncertainty, specifically in cortical lesion segmentation in multiple sclerosis using deep ensembles. The proposed analysis shifts the focus from the uncertainty-error relationship towards relevant medical and engineering factors. Our findings reveal that instance-wise uncertainty is strongly related to lesion size, shape, and cortical involvement. Expert rater feedback confirms that similar factors impede annotator confidence. Evaluations conducted on two datasets (206 patients, almost 2000 lesions) under both in-domain and distribution-shift conditions highlight the utility of the framework in different scenarios.
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
Authors: Tianyi Ren, Juampablo Heras Rivera, Hitender Oswal, Yutong Pan, Agamdeep Chopra, Jacob Ruzevick, Mehmet Kurt
Date: 2025-04-06 00:00:00
Tasks: Brain Tumor Segmentation, Image Segmentation, Medical Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Deep learning has been successfully applied to medical image segmentation, enabling accurate identification of regions of interest such as organs and lesions. This approach works effectively across diverse datasets, including those with single-image contrast, multi-contrast, and multimodal imaging data. To improve human understanding of these black-box models, there is a growing need for Explainable AI (XAI) techniques for model transparency and accountability. Previous research has primarily focused on post hoc pixel-level explanations, using methods gradient-based and perturbation-based apporaches. These methods rely on gradients or perturbations to explain model predictions. However, these pixel-level explanations often struggle with the complexity inherent in multi-contrast magnetic resonance imaging (MRI) segmentation tasks, and the sparsely distributed explanations have limited clinical relevance. In this study, we propose using contrast-level Shapley values to explain state-of-the-art models trained on standard metrics used in brain tumor segmentation. Our results demonstrate that Shapley analysis provides valuable insights into different models' behavior used for tumor segmentation. We demonstrated a bias for U-Net towards over-weighing T1-contrast and FLAIR, while Swin-UNETR provided a cross-contrast understanding with balanced Shapley distribution.
BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis
Authors: Moinak Bhattacharya, Saumya Gupta, Annie Singh, Chao Chen, Gagandeep Singh, Prateek Prasanna
Date: 2025-04-06 00:00:00
Tasks: Anatomy, Denoising, Diagnostic

Accurate brain tumor diagnosis relies on the assessment of multiple Magnetic Resonance Imaging (MRI) sequences. However, in clinical practice, the acquisition of certain sequences may be affected by factors like motion artifacts or contrast agent contraindications, leading to suboptimal outcome, such as poor image quality. This can then affect image interpretation by radiologists. Synthesizing high quality MRI sequences has thus become a critical research focus. Though recent advancements in controllable generative AI have facilitated the synthesis of diagnostic quality MRI, ensuring anatomical accuracy remains a significant challenge. Preserving critical structural relationships between different anatomical regions is essential, as even minor structural or topological inconsistencies can compromise diagnostic validity. In this work, we propose BrainMRDiff, a novel topology-preserving, anatomy-guided diffusion model for synthesizing brain MRI, leveraging brain and tumor anatomies as conditioning inputs. To achieve this, we introduce two key modules: Tumor+Structure Aggregation (TSA) and Topology-Guided Anatomy Preservation (TGAP). TSA integrates diverse anatomical structures with tumor information, forming a comprehensive conditioning mechanism for the diffusion process. TGAP enforces topological consistency during reverse denoising diffusion process; both these modules ensure that the generated image respects anatomical integrity. Experimental results demonstrate that BrainMRDiff surpasses existing baselines, achieving performance improvements of 23.33% on the BraTS-AG dataset and 33.33% on the BraTS-Met dataset. Code will be made publicly available soon.
GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRI
Authors: Levente Baljer, Ula Briski, Robert Leech, Niall J. Bourke, Kirsten A. Donald, Layla E. Bradford, Simone R. Williams, Sadia Parkar, Sidra Kaleem, Salman Osmani, Sean C. L. Deoni, Steven C. R. Williams, Rosalyn J. Moran, Emma C. Robinson, Frantisek Vasa
Date: 2025-04-06 00:00:00
Tasks: Image-to-Image Translation, Mamba, Super-Resolution

Magnetic resonance imaging (MRI) is critical for neurodevelopmental research, however access to high-field (HF) systems in low- and middle-income countries is severely hindered by their cost. Ultra-low-field (ULF) systems mitigate such issues of access inequality, however their diminished signal-to-noise ratio limits their applicability for research and clinical use. Deep-learning approaches can enhance the quality of scans acquired at lower field strengths at no additional cost. For example, Convolutional neural networks (CNNs) fused with transformer modules have demonstrated a remarkable ability to capture both local information and long-range context. Unfortunately, the quadratic complexity of transformers leads to an undesirable trade-off between long-range sensitivity and local precision. We propose a hybrid CNN and state-space model (SSM) architecture featuring a novel 3D to 1D serialisation (GAMBAS), which learns long-range context without sacrificing spatial precision. We exhibit improved performance compared to other state-of-the-art medical image-to-image translation models.
CALF: A Conditionally Adaptive Loss Function to Mitigate Class-Imbalanced Segmentation
Authors: Bashir Alam, Masa Cirkovic, Mete Harun Akcay, Md Kaf Shahrier, Sebastien Lafond, Hergys Rexha, Kurt Benke, Sepinoud Azimi, Janan Arslan
Date: 2025-04-06 00:00:00
Tasks: Segmentation

Imbalanced datasets pose a considerable challenge in training deep learning (DL) models for medical diagnostics, particularly for segmentation tasks. Imbalance may be associated with annotation quality limited annotated datasets, rare cases, or small-scale regions of interest (ROIs). These conditions adversely affect model training and performance, leading to segmentation boundaries which deviate from the true ROIs. Traditional loss functions, such as Binary Cross Entropy, replicate annotation biases and limit model generalization. We propose a novel, statistically driven, conditionally adaptive loss function (CALF) tailored to accommodate the conditions of imbalanced datasets in DL training. It employs a data-driven methodology by estimating imbalance severity using statistical methods of skewness and kurtosis, then applies an appropriate transformation to balance the training dataset while preserving data heterogeneity. This transformative approach integrates a multifaceted process, encompassing preprocessing, dataset filtering, and dynamic loss selection to achieve optimal outcomes. We benchmark our method against conventional loss functions using qualitative and quantitative evaluations. Experiments using large-scale open-source datasets (i.e., UPENN-GBM, UCSF, LGG, and BraTS) validate our approach, demonstrating substantial segmentation improvements. Code availability: https://anonymous.4open.science/r/MICCAI-Submission-43F9/.