Journal of Medical Imaging最新文献

Purpose: Biliary tract cancer, also known as intrahepatic cholangiocarcinoma (IHCC), is a rare disease that shows no clear symptoms during its early stage, but its prognosis depends highly on the cancer subtype. Hence, an accurate cancer subtype classification model is necessary to provide better treatment plans to patients and to reduce mortality. However, annotating histopathology images at the pixel or patch level is time-consuming and labor-intensive for giga-pixel whole slide images. To address this problem, we propose a weakly supervised method for classifying IHCC subtypes using only image-level labels.

Approach: The core idea of the proposed method is to detect regions (i.e., subimages or patches) commonly included in all subtypes, which we name the "hidden class," and to remove them via iterative application of contrastive loss and label smoothing. Doing so will enable us to obtain only patches that faithfully represent each subtype, which are then used to train the image-level classification model by multiple instance learning (MIL).

Results: Our method outperforms the state-of-the-art weakly supervised learning methods ABMIL, TransMIL, and DTFD-MIL by $\sim 17\%$ , 18%, and 8%, respectively, and achieves performance comparable to that of supervised methods.

Conclusions: The introduction of a hidden class to represent patches commonly found across all subtypes enhances the accuracy of IHCC classification and addresses the weak labeling problem in histopathology images.

Purpose: Various deep learning (DL) approaches have been developed for estimating scatter radiation in digital breast tomosynthesis (DBT). Existing DL methods generally employ an end-to-end training approach, overlooking the underlying physics of scatter formation. We propose a deep learning approach inspired by asymmetric scatter kernel superposition to estimate scatter in DBT.

Approach: We use the network to generate the scatter amplitude distribution as well as the scatter kernel width and asymmetric factor map. To account for variations in local breast thickness and shape in DBT projection data, we integrated the Euclidean distance map and projection angle information into the network design for estimating the asymmetric factor.

Results: Systematic experiments on numerical phantom data and physical experimental data demonstrated the outperformance of the proposed approach to UNet-based end-to-end scatter estimation and symmetric kernel-based approaches in terms of signal-to-noise ratio and structure similarity index measure of the resulting scatter corrected images.

Conclusions: The proposed method is believed to have achieved significant advancement in scatter estimation of DBT projections, allowing a robust and reliable physics-informed scatter correction.

Purpose: We examine the transformative potential of artificial intelligence (AI) in medical imaging diagnosis, focusing on improving diagnostic accuracy and efficiency through advanced algorithms. It addresses the significant challenges preventing immediate clinical adoption of AI, specifically from technical, ethical, and legal perspectives. The aim is to highlight the current state of AI in medical imaging and outline the necessary steps to ensure safe, effective, and ethically sound clinical implementation.

Approach: We conduct a comprehensive discussion, with special emphasis on the technical requirements for robust AI models, the ethical frameworks needed for responsible deployment, and the legal implications, including data privacy and regulatory compliance. Explainable artificial intelligence (XAI) is examined as a means to increase transparency and build trust among healthcare professionals and patients.

Results: The analysis reveals key challenges to AI integration in clinical settings, including the need for extensive high-quality datasets, model reliability, advanced infrastructure, and compliance with regulatory standards. The lack of explainability in AI outputs remains a barrier, with XAI identified as crucial for meeting transparency standards and enhancing trust among end users.

Conclusions: Overcoming these barriers requires a collaborative, multidisciplinary approach to integrate AI into clinical practice responsibly. Addressing technical, ethical, and legal issues will support a softer transition, fostering a more accurate, efficient, and patient-centered healthcare system where AI augments traditional medical practices.

Purpose: Multiple instance learning (MIL) has emerged as the best solution for whole slide image (WSI) classification. It consists of dividing each slide into patches, which are treated as a bag of instances labeled with a global label. MIL includes two main approaches: instance-based and embedding-based. In the former, each patch is classified independently, and then, the patch scores are aggregated to predict the bag label. In the latter, bag classification is performed after aggregating patch embeddings. Even if instance-based methods are naturally more interpretable, embedding-based MILs have usually been preferred in the past due to their robustness to poor feature extractors. Recently, the quality of feature embeddings has drastically increased using self-supervised learning (SSL). Nevertheless, many authors continue to endorse the superiority of embedding-based MIL.

Approach: We conduct 710 experiments across 4 datasets, comparing 10 MIL strategies, 6 self-supervised methods with 4 backbones, 4 foundation models, and various pathology-adapted techniques. Furthermore, we introduce 4 instance-based MIL methods, never used before in the pathology domain.

Results: We show that with a good SSL feature extractor, simple instance-based MILs, with very few parameters, obtain similar or better performance than complex, state-of-the-art (SOTA) embedding-based MIL methods, setting new SOTA results on the BRACS and Camelyon16 datasets.

Conclusion: As simple instance-based MIL methods are naturally more interpretable and explainable to clinicians, our results suggest that more effort should be put into well-adapted SSL methods for WSI rather than into complex embedding-based MIL methods.

Purpose: We propose a deep learning framework, the cycle-guided denoising diffusion probability model (CG-DDPM), for cross-modality magnetic resonance imaging (MRI) synthesis. The CG-DDPM aims to generate high-quality MRIs of a target modality from an existing modality, addressing the challenge of missing MRI sequences in clinical practice.

Approach: The CG-DDPM employs two interconnected conditional diffusion probabilistic models, with a cycle-guided reverse latent noise regularization to enhance synthesis consistency and anatomical fidelity. The framework was evaluated using the BraTS2020 dataset, which includes three-dimensional brain MRIs with $T1$ -weighted, $T2$ -weighted, and FLAIR modalities. The synthetic images were quantitatively assessed using metrics such as multi-scale structural similarity measure (MSSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE). The CG-DDPM was benchmarked against state-of-the-art methods, including IDDPM, IDDIM, and MRI-cGAN.

Results: The CG-DDPM demonstrated superior performance across all cross-modality synthesis tasks (T1 → T2, T2 → T1, T1 → FLAIR, and FLAIR → T1). It consistently achieved the highest MSSIM values (ranging from 0.966 to 0.971), the lowest MAE (0.011 to 0.013), and competitive PSNR values (27.7 to 28.8 dB). Across all tasks, CG-DDPM outperformed IDDPM, IDDIM, and MRI-cGAN in most metrics and exhibited significantly lower uncertainty and inconsistency in MC-based sampling. Statistical analyses confirmed the robustness of CG-DDPM, with $p\text{-}\text{values}<0.05$ in key comparisons.

Conclusions: The proposed CG-DDPM provides a robust and efficient solution for cross-modality MRI synthesis, offering improved accuracy, stability, and clinical applicability compared with existing methods. This approach has the potential to streamline MRI-based workflows, enhance diagnostic imaging, and support precision treatment planning in medical physics and radiation oncology.

Purpose: Autism is one of the most common neurodevelopmental conditions, and it is characterized by restricted, repetitive behaviors and social difficulties that affect daily functioning. It is challenging to provide an early and accurate diagnosis due to the wide diversity of symptoms and the developmental changes that occur during childhood. We evaluate the feasibility of an explainable deep learning (DL) model using structural MRI (sMRI) to identify meaningful brain biomarkers relevant to autism in children and thus support its diagnosis.

Approach: A total of 452 $T1$ -weighted sMRI scans from children aged 9 to 11 years were obtained from the Autism Brain Imaging Data Exchange database. A DL model was trained to differentiate between autistic and typically developing children. Model explainability was assessed using saliency maps to identify key brain regions contributing to classification. Model performance was evaluated across 20 folds and compared with traditional machine learning models trained with regional volumetric features extracted from the sMRI scans.

Results: The model achieved a mean area under the receiver operating curve of 71.2%. The saliency maps highlighted brain regions that are known neuroanatomical and functional biomarkers associated with autism, such as the cuneus, pericalcarine, ventricles, lingual, vermal lobules, caudate, and thalamus.

Conclusions: We show the potential of interpretable DL models trained on sMRI data to aid in autism diagnosis within a narrowly defined pediatric age group. Our findings contribute to the field of explainable artificial intelligence methods in neurodevelopmental research and may help in clinical decision-making for autism and other neurodevelopmental conditions.

Purpose: The purpose of this study was to assess the variations in shear-wave speed (SWS) in individual thenar muscles under varied pinch forces in healthy adults. It was hypothesized that (1) SWS would vary among the individual thenar muscles, and (2) there would be an increase in SWS with increased pinch force.

Approach: Thirteen healthy participants' dominant hands were imaged using an ultrasound probe aligned longitudinally along the muscle fibers of the abductor pollicis brevis (APB), opponens pollicis (OPP), and flexor pollicis brevis (FPB). The SWS of each muscle was derived. Each participant completed trials consisting of randomly ordered pinch forces at 0, 10, and 20 N, 10% of maximum pinch force (MPF), and 20% MPF.

Results: The SWSs varied significantly among individual thenar muscles ( $p<0.01$ ) under absolute ( $p<0.01$ ) and relative forces ( $p<0.05$ ). There was a significant increase in SWS as the force increased from 0 to 20 N in the APB ( $p<0.001$ ) and OPP ( $p<0.001$ ), and not in the FPB ( $p=0.873$ ). There was a significant increase in SWS as the force increased from 0 to 20% MPF in the APB ( $p=0.005$ ), and not in the OPP ( $p=0.586$ ) or the FPB ( $p=0.984$ ).

Conclusions: The SWS of the APB and OPP increased as force increased and was different among the thenar muscles. This suggests SWS evaluations may be an appropriate method for evaluating muscles under tension, or different voluntary force conditions, specifically for the APB and OPP muscles.

Purpose: Weakly supervised learning (WSL) is widely used for histopathological image analysis by modeling images as sets of fixed-size patches and utilizing image-level diagnoses as weak labels. However, in multiclass classification scenarios, patches corresponding to a wide spectrum of diagnostic categories can co-exist in a single image, complicating the learning process. We aim to address label uncertainty in such multiclass settings.

Approach: We propose a two-branch architecture and a complementary training strategy to improve patch-based WSL. One branch estimates patch-level class likelihoods, whereas the other predicts per-class patch relevance weights. These outputs are combined into image-level class predictions via a relevance-weighted sum of per-patch class likelihoods. To further improve performance, we introduce a multilabel augmentation strategy that forms new training samples by combining patch sets and labels from pairs of images, resulting in multilabel samples that enrich the training set by increasing the chance of having more patches that are relevant to the augmented label sets.

Results: We evaluate our method on two challenging multiclass breast histopathology datasets for region of interest classification. The proposed architecture and training strategy outperform conventional weakly supervised methods, demonstrating improved classification accuracy and robustness, particularly in underrepresented classes.

Conclusions: The proposed architecture effectively models the complex relationship between image-level labels and patch-level content in multiclass histopathological image analysis. Combined with the image-level multilabel augmentation strategy, it improves learning under label uncertainty. These contributions hold potential for more accurate and scalable diagnostic support systems in digital pathology.