Registration-Enhanced Segmentation Method for Prostate Cancer in Ultrasound Images

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.

Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)

Cancer remains one of the leading causes of mortality worldwide, and among its many forms, brain tumors are particularly notorious due to their aggressive nature and the critical challenges involved in early diagnosis. Recent advances in artificial intelligence have shown great promise in assisting medical professionals with precise tumor segmentation, a key step in timely diagnosis and treatment planning. However, many state-of-the-art segmentation methods require extensive computational resources and prolonged training times, limiting their practical application in resource-constrained settings. In this work, we present a novel dual-decoder U-Net architecture enhanced with attention-gated skip connections, designed specifically for brain tumor segmentation from MRI scans. Our approach balances efficiency and accuracy by achieving competitive segmentation performance while significantly reducing training demands. Evaluated on the BraTS 2020 dataset, the proposed model achieved Dice scores of 85.06% for Whole Tumor (WT), 80.61% for Tumor Core (TC), and 71.26% for Enhancing Tumor (ET) in only 50 epochs, surpassing several commonly used U-Net variants. Our model demonstrates that high-quality brain tumor segmentation is attainable even under limited computational resources, thereby offering a viable solution for researchers and clinicians operating with modest hardware. This resource-efficient model has the potential to improve early detection and diagnosis of brain tumors, ultimately contributing to better patient outcomes

Brain Tumor Identification using Improved YOLOv8

Identifying the extent of brain tumors is a significant challenge in brain cancer treatment. The main difficulty is in the approximate detection of tumor size. Magnetic resonance imaging (MRI) has become a critical diagnostic tool. However, manually detecting the boundaries of brain tumors from MRI scans is a labor-intensive task that requires extensive expertise. Deep learning and computer-aided detection techniques have led to notable advances in machine learning for this purpose. In this paper, we propose a modified You Only Look Once (YOLOv8) model to accurately detect the tumors within the MRI images. The proposed model replaced the Non-Maximum Suppression (NMS) algorithm with a Real-Time Detection Transformer (RT- DETR) in the detection head. NMS filters out redundant or overlapping bounding boxes in the detected tumors, but they are hand-designed and pre-set. RT-DETR removes hand-designed components. The second improvement was made by replacing the normal convolution block with ghost convolution. Ghost Convolution reduces computational and memory costs while maintaining high accuracy and enabling faster inference, making it ideal for resource-constrained environments and real-time applications. The third improvement was made by introducing a vision transformer block in the backbone of YOLOv8 to extract context-aware features. We used a publicly available dataset of brain tumors in the proposed model. The proposed model performed better than the original YOLOv8 model and also performed better than other object detectors (Faster R- CNN, Mask R-CNN, YOLO, YOLOv3, YOLOv4, YOLOv5, SSD, RetinaNet, EfficientDet, and DETR). The proposed model achieved 0.91 mAP (mean Average Precision)@0.5.

Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.

How Good is my Histopathology Vision-Language Foundation Model? A Holistic Benchmark

Recently, histopathology vision-language foundation models (VLMs) have gained popularity due to their enhanced performance and generalizability across different downstream tasks. However, most existing histopathology benchmarks are either unimodal or limited in terms of diversity of clinical tasks, organs, and acquisition instruments, as well as their partial availability to the public due to patient data privacy. As a consequence, there is a lack of comprehensive evaluation of existing histopathology VLMs on a unified benchmark setting that better reflects a wide range of clinical scenarios. To address this gap, we introduce HistoVL, a fully open-source comprehensive benchmark comprising images acquired using up to 11 various acquisition tools that are paired with specifically crafted captions by incorporating class names and diverse pathology descriptions. Our Histo-VL includes 26 organs, 31 cancer types, and a wide variety of tissue obtained from 14 heterogeneous patient cohorts, totaling more than 5 million patches obtained from over 41K WSIs viewed under various magnification levels. We systematically evaluate existing histopathology VLMs on Histo-VL to simulate diverse tasks performed by experts in real-world clinical scenarios. Our analysis reveals interesting findings, including large sensitivity of most existing histopathology VLMs to textual changes with a drop in balanced accuracy of up to 25% in tasks such as Metastasis detection, low robustness to adversarial attacks, as well as improper calibration of models evident through high ECE values and low model prediction confidence, all of which can affect their clinical implementation.

SCFANet: Style Distribution Constraint Feature Alignment Network For Pathological Staining Translation

Immunohistochemical (IHC) staining serves as a valuable technique for detecting specific antigens or proteins through antibody-mediated visualization. However, the IHC staining process is both time-consuming and costly. To address these limitations, the application of deep learning models for direct translation of cost-effective Hematoxylin and Eosin (H&E) stained images into IHC stained images has emerged as an efficient solution. Nevertheless, the conversion from H&E to IHC images presents significant challenges, primarily due to alignment discrepancies between image pairs and the inherent diversity in IHC staining style patterns. To overcome these challenges, we propose the Style Distribution Constraint Feature Alignment Network (SCFANet), which incorporates two innovative modules: the Style Distribution Constrainer (SDC) and Feature Alignment Learning (FAL). The SDC ensures consistency between the generated and target images' style distributions while integrating cycle consistency loss to maintain structural consistency. To mitigate the complexity of direct image-to-image translation, the FAL module decomposes the end-to-end translation task into two subtasks: image reconstruction and feature alignment. Furthermore, we ensure pathological consistency between generated and target images by maintaining pathological pattern consistency and Optical Density (OD) uniformity. Extensive experiments conducted on the Breast Cancer Immunohistochemical (BCI) dataset demonstrate that our SCFANet model outperforms existing methods, achieving precise transformation of H&E-stained images into their IHC-stained counterparts. The proposed approach not only addresses the technical challenges in H&E to IHC image translation but also provides a robust framework for accurate and efficient stain conversion in pathological analysis.

A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model performance, taking into account minimum child weight and learning rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM, Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted. Furthermore, the correlation between features and targets is examined using the deep neural network (DNN) model and thus their capability in detecting complex patternsis established. It is argued that several ML models can be capable of classifying lung cancer stages with great accuracy. In spite of the complexity of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic Regression excel with superior performance. The models perform better than the others in lung cancer prediction on the complete set of comparative metrics like accuracy, precision, recall, and F-1 score

SYN-LUNGS: Towards Simulating Lung Nodules with Anatomy-Informed Digital Twins for AI Training

AI models for lung cancer screening are limited by data scarcity, impacting generalizability and clinical applicability. Generative models address this issue but are constrained by training data variability. We introduce SYN-LUNGS, a framework for generating high-quality 3D CT images with detailed annotations. SYN-LUNGS integrates XCAT3 phantoms for digital twin generation, X-Lesions for nodule simulation (varying size, location, and appearance), and DukeSim for CT image formation with vendor and parameter variability. The dataset includes 3,072 nodule images from 1,044 simulated CT scans, with 512 lesions and 174 digital twins. Models trained on clinical + simulated data outperform clinical only models, achieving 10% improvement in detection, 2-9% in segmentation and classification, and enhanced synthesis.By incorporating anatomy-informed simulations, SYN-LUNGS provides a scalable approach for AI model development, particularly in rare disease representation and improving model reliability.

Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence

Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced Spectral Mammography (CESM), a second-level imaging technique, offers enhanced accuracy in tumor detection. Nonetheless, its application is restricted due to higher radiation exposure, the use of contrast agents, and limited accessibility. As a result, CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM despite the superior diagnostic performance of CESM. While biopsy remains the gold standard for definitive diagnosis, it is an invasive procedure that can cause discomfort for patients. We introduce a multimodal, multi-view deep learning approach for virtual biopsy, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign. To address the challenge of missing CESM data, we leverage generative artificial intelligence to impute CESM images from FFDM scans. Experimental results demonstrate that incorporating the CESM modality is crucial to enhance the performance of virtual biopsy. When real CESM data is missing, synthetic CESM images proved effective, outperforming the use of FFDM alone, particularly in multimodal configurations that combine FFDM and CESM modalities. The proposed approach has the potential to improve diagnostic workflows, providing clinicians with augmented intelligence tools to improve diagnostic accuracy and patient care. Additionally, as a contribution to the research community, we publicly release the dataset used in our experiments, facilitating further advancements in this field.

Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer

Purpose: This study examines the core traits of image-to-image translation (I2I) networks, focusing on their effectiveness and adaptability in everyday clinical settings. Methods: We have analyzed data from 794 patients diagnosed with prostate cancer (PCa), using ten prominent 2D/3D I2I networks to convert ultrasound (US) images into MRI scans. We also introduced a new analysis of Radiomic features (RF) via the Spearman correlation coefficient to explore whether networks with high performance (SSIM>85%) could detect subtle RFs. Our study further examined synthetic images by 7 invited physicians. As a final evaluation study, we have investigated the improvement that are achieved using the synthetic MRI data on two traditional machine learning and one deep learning method. Results: In quantitative assessment, 2D-Pix2Pix network substantially outperformed the other 7 networks, with an average SSIM~0.855. The RF analysis revealed that 76 out of 186 RFs were identified using the 2D-Pix2Pix algorithm alone, although half of the RFs were lost during the translation process. A detailed qualitative review by 7 medical doctors noted a deficiency in low-level feature recognition in I2I tasks. Furthermore, the study found that synthesized image-based classification outperformed US image-based classification with an average accuracy and AUC~0.93. Conclusion: This study showed that while 2D-Pix2Pix outperformed cutting-edge networks in low-level feature discovery and overall error and similarity metrics, it still requires improvement in low-level feature performance, as highlighted by Group 3. Further, the study found using synthetic image-based classification outperformed original US image-based methods.