Deep learning models often hallucinate, producing realistic artifacts that are not truly present in the sample. This can have dire consequences for scientific and medical inverse problems, such as MRI and microscopy denoising, where accuracy is more important than perceptual quality. Uncertainty quantification techniques, such as conformal prediction, can pinpoint outliers and provide guarantees for image regression tasks, improving reliability. However, existing methods utilize a linear constant scaling factor to calibrate uncertainty bounds, resulting in larger, less informative bounds. We propose QUTCC, a quantile uncertainty training and calibration technique that enables nonlinear, non-uniform scaling of quantile predictions to enable tighter uncertainty estimates. Using a U-Net architecture with a quantile embedding, QUTCC enables the prediction of the full conditional distribution of quantiles for the imaging task. During calibration, QUTCC generates uncertainty bounds by iteratively querying the network for upper and lower quantiles, progressively refining the bounds to obtain a tighter interval that captures the desired coverage. We evaluate our method on several denoising tasks as well as compressive MRI reconstruction. Our method successfully pinpoints hallucinations in image estimates and consistently achieves tighter uncertainty intervals than prior methods while maintaining the same statistical coverage.

Medical imaging plays a vital role in early disease diagnosis and monitoring. Specifically, blood microscopy offers valuable insights into blood cell morphology and the detection of hematological disorders. In recent years, deep learning-based automated classification systems have demonstrated high potential in enhancing the accuracy and efficiency of blood image analysis. However, a detailed performance analysis of specific deep learning frameworks appears to be lacking. This paper compares the performance of three popular deep learning frameworks, TensorFlow with Keras, PyTorch, and JAX, in classifying blood cell images from the publicly available BloodMNIST dataset. The study primarily focuses on inference time differences, but also classification performance for different image sizes. The results reveal variations in performance across frameworks, influenced by factors such as image resolution and framework-specific optimizations. Classification accuracy for JAX and PyTorch was comparable to current benchmarks, showcasing the efficiency of these frameworks for medical image classification.

Magnetic Resonance Imaging (MRI) enables the acquisition of multiple image contrasts, such as T1-weighted (T1w) and T2-weighted (T2w) scans, each offering distinct diagnostic insights. However, acquiring all desired modalities increases scan time and cost, motivating research into computational methods for cross-modal synthesis. To address this, recent approaches aim to synthesize missing MRI contrasts from those already acquired, reducing acquisition time while preserving diagnostic quality. Image-to-image (I2I) translation provides a promising framework for this task. In this paper, we present a comprehensive benchmark of generative models$\unicode{x2013}$specifically, Generative Adversarial Networks (GANs), diffusion models, and flow matching (FM) techniques$\unicode{x2013}$for T1w-to-T2w 2D MRI I2I translation. All frameworks are implemented with comparable settings and evaluated on three publicly available MRI datasets of healthy adults. Our quantitative and qualitative analyses show that the GAN-based Pix2Pix model outperforms diffusion and FM-based methods in terms of structural fidelity, image quality, and computational efficiency. Consistent with existing literature, these results suggest that flow-based models are prone to overfitting on small datasets and simpler tasks, and may require more data to match or surpass GAN performance. These findings offer practical guidance for deploying I2I translation techniques in real-world MRI workflows and highlight promising directions for future research in cross-modal medical image synthesis. Code and models are publicly available at https://github.com/AndreaMoschetto/medical-I2I-benchmark.

Magnetic resonance imaging (MRI) has become a pivotal non-invasive tool in the evaluation and management of lymphedema. This review systematically summarizes its current applications, highlighting imaging techniques, comparative advantages over other modalities, MRI-based staging systems, and emerging clinical roles. A comprehensive literature review was conducted, covering comparisons with lymphoscintigraphy, ultrasound, and computed tomography (CT), as well as studies on the feasibility of multiparametric MRI sequences. Compared to conventional imaging, MRI offers superior soft tissue contrast and enables detailed assessment of lymphatic anatomy, tissue composition, and fluid distribution through sequences such as T2-weighted imaging, diffusion-weighted imaging (DWI), and magnetic resonance lymphangiography (MRL). Standardized grading systems have been proposed to support clinical staging. MRI is increasingly applied in preoperative planning and postoperative surveillance.These findings underscore MRI's diagnostic precision and clinical utility. Future research should focus on protocol standardization, incorporation of quantitative biomarkers, and development of AI-driven tools to enable personalized, scalable lymphedema care.

Hematoma expansion (HE), including intraventricular hemorrhage (IVH) growth, significantly affects outcomes in patients with intracerebral hemorrhage (ICH). This study aimed to develop, validate, and interpret a deep learning model, HENet, for predicting three definitions of HE. Using CT scans and clinical data from 718 ICH patients across three hospitals, the multicenter retrospective study focused on revised hematoma expansion (RHE) definitions 1 and 2, and conventional HE (CHE). HENet's performance was compared with 2D models and physician predictions using two external validation sets. Results showed that HENet achieved high AUC values for RHE1, RHE2, and CHE predictions, surpassing physicians' predictions and 2D models in net reclassification index and integrated discrimination index for RHE1 and RHE2 outcomes. The Grad-CAM technique provided visual insights into the model's decision-making process. These findings suggest that integrating HENet into clinical practice could improve prediction accuracy and patient outcomes in ICH cases.

With the widespread application of machine learning (ML) in the diagnosis and treatment of colorectal cancer (CRC), some studies have investigated the use of ML techniques for the diagnosis of KRAS (Kirsten rat sarcoma) mutation. Nevertheless, there is scarce evidence from evidence-based medicine to substantiate its efficacy. Our study was carried out to systematically review the performance of ML models developed using different modeling approaches, in diagnosing KRAS mutations in CRC. We aim to offer evidence-based foundations for the development and enhancement of future intelligent diagnostic tools. PubMed, Cochrane Library, Embase, and Web of Science were systematically retrieved, with the search cutoff date set to December 22, 2024. The encompassed studies are publicly published research papers that use ML to diagnose KRAS gene mutations in CRC. The risk of bias in the encompassed models was evaluated via the PROBAST (Prediction Model Risk of Bias Assessment Tool). A meta-analysis of the model's concordance index (c-index) was performed, and a bivariate mixed-effects model was used to summarize sensitivity and specificity based on diagnostic contingency tables. A total of 43 studies involving 10,888 patients were included. The modeling variables were derived from clinical characteristics, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography/computed tomography, and pathological histology. In the validation cohort, for the ML model developed based on CT radiomic features, the c-index, sensitivity, and specificity were 0.87 (95% CI 0.84-0.90), 0.85 (95% CI 0.80-0.89), and 0.83 (95% CI 0.73-0.89), respectively. For the model developed using MRI radiomic features, the c-index, sensitivity, and specificity were 0.77 (95% CI 0.71-0.83), 0.78 (95% CI 0.72-0.83), and 0.73 (95% CI 0.63-0.81), respectively. For the ML model developed based on positron emission tomography/computed tomography radiomic features, the c-index, sensitivity, and specificity were 0.84 (95% CI 0.77-0.90), 0.73, and 0.83, respectively. Notably, the deep learning (DL) model based on pathological images demonstrated a c-index, sensitivity, and specificity of 0.96 (95% CI 0.94-0.98), 0.83 (95% CI 0.72-0.91), and 0.87 (95% CI 0.77-0.92), respectively. The DL model MRI-based model showed a c-index of 0.93 (95% CI 0.90-0.96), sensitivity of 0.85 (95% CI 0.75-0.91), and specificity of 0.83 (95% CI 0.77-0.88). ML is highly accurate in diagnosing KRAS mutations in CRC, and DL models based on MRI and pathological images exhibit particularly strong diagnosis accuracy. More broadly applicable DL-based diagnostic tools may be developed in the future. However, the clinical application of DL models remains relatively limited at present. Therefore, future research should focus on increasing sample sizes, improving model architectures, and developing more advanced DL models to facilitate the creation of highly efficient intelligent diagnostic tools for KRAS mutation diagnosis in CRC.

The Transformer architecture has demonstrated remarkable results in 3D medical image segmentation due to its capability of modeling global relationships. However, it poses a significant computational burden when processing high-dimensional medical images. Mamba, as a State Space Model (SSM), has recently emerged as a notable approach for modeling long-range dependencies in sequential data. Although a substantial amount of Mamba-based research has focused on natural language and 2D image processing, few studies explore the capability of Mamba on 3D medical images. In this paper, we propose SegMamba-V2, a novel 3D medical image segmentation model, to effectively capture long-range dependencies within whole-volume features at each scale. To achieve this goal, we first devise a hierarchical scale downsampling strategy to enhance the receptive field and mitigate information loss during downsampling. Furthermore, we design a novel tri-orientated spatial Mamba block that extends the global dependency modeling process from one plane to three orthogonal planes to improve feature representation capability. Moreover, we collect and annotate a large-scale dataset (named CRC-2000) with fine-grained categories to facilitate benchmarking evaluation in 3D colorectal cancer (CRC) segmentation. We evaluate the effectiveness of our SegMamba-V2 on CRC-2000 and three other large-scale 3D medical image segmentation datasets, covering various modalities, organs, and segmentation targets. Experimental results demonstrate that our Segmamba-V2 outperforms state-of-the-art methods by a significant margin, which indicates the universality and effectiveness of the proposed model on 3D medical image segmentation tasks. The code for SegMamba-V2 is publicly available at: https://github.com/ge-xing/SegMamba-V2.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognostication. Studies were evaluated for the type of input data (histology and whether CT, PET, and MRI were used), cancer therapy intervention, prognosis performance, and comparisons to clinical prognosis systems such as TNM staging. Further comparisons were drawn between different types of AI, such as machine learning (ML) and deep learning (DL). Syntheses of therapeutic interventions and algorithm input modalities were performed for comparison purposes. The review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The initial database identified 3880 results, which were reduced to 513 after the automatic screening, and 309 after the exclusion criteria. The prognostic performance of AI for NSCLC has been investigated using histology and genetic data, and CT, PET, and MR imaging for surgery, immunotherapy, and radiation therapy patients with and without chemotherapy. Studies per therapy intervention were 13 for immunotherapy, 10 for radiotherapy, 14 for surgery, and 34 for other, multiple, or no specific therapy. The results of this systematic review demonstrate that AI-based prognostication methods consistently present higher prognostic performance for NSCLC, especially when directly compared with traditional prognostication techniques such as TNM staging. The use of DL outperforms ML-based prognostication techniques. DL-based prognostication demonstrates the potential for personalized precision cancer therapy as a supplementary decision-making tool. Before it is fully utilized in clinical practice, it is recommended that it be thoroughly validated through well-designed clinical trials.

We conducted a systematic review and meta-analysis in diagnostic performance of studies that tried to use artificial intelligence (AI) algorithms in detecting pancreatic ductal adenocarcinoma (PDAC) and distinguishing them from other types of pancreatic lesions. We systematically searched for studies on pancreatic lesions and AI from January 2014 to May 2024. Data were extracted and a meta-analysis was performed using contingency tables and a random-effects model to calculate pooled sensitivity and specificity. Quality assessment was done using modified TRIPOD and PROBAST tools. We included 26 studies in this systematic review, with 22 studies chosen for meta-analysis. The evaluation of AI algorithms' performance in internal validation exhibited a pooled sensitivity of 93% (95% confidence interval [CI], 90 to 95) and specificity of 95% (95% CI, 92 to 97). Additionally, externally validated AI algorithms demonstrated a combined sensitivity of 89% (95% CI, 85 to 92) and specificity of 91% (95% CI, 85 to 95). Subgroup analysis indicated that diagnostic performance differed by comparator group, image contrast, segmentation technique, and algorithm type, with contrast-enhanced imaging and specific AI models (e.g., random forest for sensitivity and CNN for specificity) demonstrating superior accuracy. Although the potential biases should be further addressed, results of this systematic review and meta-analysis showed that AI models have the potential to be incorporated in clinical settings for the detection of smaller tumors and underpinning early signs of PDAC.

Detecting small pancreatic ductal adenocarcinomas (PDAC) is challenging owing to their difficulty in being identified as distinct tumor masses. This study assesses the diagnostic performance of a three-dimensional convolutional neural network for the automatic detection of small PDAC using both automatic tumor mass detection and indirect indicator evaluation. High-resolution contrast-enhanced computed tomography (CT) scans from 181 patients diagnosed with PDAC (diameter ≤ 2 cm) between January 2018 and December 2023 were analyzed. The D/P ratio, which is the cross-sectional area of the MPD to that of the pancreatic parenchyma, was identified as an indirect indicator. A total of 204 patient data sets including 104 normal controls were analyzed for automatic tumor mass detection and D/P ratio evaluation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated to detect tumor mass. The sensitivity of PDAC detection was compared with that of the software and radiologists, and tumor localization accuracy was validated against endoscopic ultrasonography (EUS) findings. The sensitivity, specificity, PPV, and NPV for tumor mass detection were 77.0%, 76.0%, 75.5%, and 77.5%, respectively; for D/P ratio detection, 87.0%, 94.2%, 93.5%, and 88.3%, respectively; and for combined tumor mass and D/P ratio detections, 96.0%, 70.2%, 75.6%, and 94.8%, respectively. No significant difference was observed between the software's sensitivity and that of the radiologist's report (software, 96.0%; radiologist, 96.0%; p = 1). The concordance rate between software findings and EUS was 96.0%. Combining indirect indicator evaluation with tumor mass detection may improve small PDAC detection accuracy.