The test-time adaptation (TTA) of deep-learning-based semantic segmentation models, specific to individual patient data, was addressed in this study. The existing TTA methods in medical imaging are often unconstrained, require anatomical prior information or additional neural networks built during training phase, making them less practical, and prone to performance deterioration. In this study, a novel framework based on information geometric principles was proposed to achieve generic, off-the-shelf, regularized patient-specific adaptation of models during test-time. By considering the pre-trained model and the adapted models as part of statistical neuromanifolds, test-time adaptation was treated as constrained functional regularization using information geometric measures, leading to improved generalization and patient optimality. The efficacy of the proposed approach was shown on three challenging problems: 1) improving generalization of state-of-the-art models for segmenting COVID-19 anomalies in Computed Tomography (CT) images 2) cross-institutional brain tumor segmentation from magnetic resonance (MR) images, 3) segmentation of retinal layers in Optical Coherence Tomography (OCT) images. Further, it was demonstrated that robust patient-specific adaptation can be achieved without adding significant computational burden, making it first of its kind based on information geometric principles.

A diagnostic medical foundation model (MedFM) is an artificial intelligence (AI) system engineered to accurately determine diagnoses across various medical imaging modalities and specialties. To train MedFM, we created the PubMed Central Medical Images Dataset (PMCMID), the largest annotated medical image dataset to date, comprising 16,126,659 images from 3,021,780 medical publications. Using AI- and ontology-based methods, we identified 4,482,237 medical images (e.g., clinical photos, X-rays, ultrasounds) and generated comprehensive annotations. To optimize MedFMs performance and assess biases, 13,266 images were manually annotated to establish a multimodal benchmark. MedFM achieved physician-level performance in diagnosis tasks spanning radiology, dermatology, and infectious diseases without requiring specific training. Additionally, we developed the Image2Paper app, allowing clinicians to upload medical images and retrieve relevant literature. The correct diagnoses appeared within the top ten results in 88.4% and at least one relevant differential diagnosis in 93.0%. MedFM and PMCMID were made publicly available. FundingResearch reported here was partially supported by the National Cancer Institute (NCI) (R01 CA260271), the Saudi Company for Artificial Intelligence (SCAI) Authority, and the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the project DAKI-FWS (01MK21009E). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The diagnosis of prostate cancer increasingly depends on multimodal imaging, particularly magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS). However, accurate registration between these modalities remains a fundamental challenge due to the differences in dimensionality and anatomical representations. In this work, we present a novel framework that addresses these challenges through a two-stage process: TRUS 3D reconstruction followed by cross-modal registration. Unlike existing TRUS 3D reconstruction methods that rely heavily on external probe tracking information, we propose a totally probe-location-independent approach that leverages the natural correlation between sagittal and transverse TRUS views. With the help of our clustering-based feature matching method, we enable the spatial localization of 2D frames without any additional probe tracking information. For the registration stage, we introduce an unsupervised diffusion-based framework guided by modality translation. Unlike existing methods that translate one modality into another, we map both MR and US into a pseudo intermediate modality. This design enables us to customize it to retain only registration-critical features, greatly easing registration. To further enhance anatomical alignment, we incorporate an anatomy-aware registration strategy that prioritizes internal structural coherence while adaptively reducing the influence of boundary inconsistencies. Extensive validation demonstrates that our approach outperforms state-of-the-art methods by achieving superior registration accuracy with physically realistic deformations in a completely unsupervised fashion.

With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, particularly generative models, presents a promising solution. These models can create diverse and comparable imaging datasets without compromising patient privacy, thereby supporting modern medical education. In this study, we explore the use of convolutional neural networks (CNNs) and CycleGAN (Zhu et al., 2017) for generating synthetic medical images. The source code is available at https://github.com/mliuby/COMP4211-Project.

With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, particularly generative models, presents a promising solution. These models can create diverse and comparable imaging datasets without compromising patient privacy, thereby supporting modern medical education. In this study, we explore the use of convolutional neural networks (CNNs) and CycleGAN (Zhu et al., 2017) for generating synthetic medical images. The source code is available at https://github.com/mliuby/COMP4211-Project.

Clinical decision-making routinely demands reasoning over heterogeneous data, yet existing multimodal language models (MLLMs) remain largely vision-centric and fail to generalize across clinical specialties. To bridge this gap, we introduce QoQ-Med-7B/32B, the first open generalist clinical foundation model that jointly reasons across medical images, time-series signals, and text reports. QoQ-Med is trained with Domain-aware Relative Policy Optimization (DRPO), a novel reinforcement-learning objective that hierarchically scales normalized rewards according to domain rarity and modality difficulty, mitigating performance imbalance caused by skewed clinical data distributions. Trained on 2.61 million instruction tuning pairs spanning 9 clinical domains, we show that DRPO training boosts diagnostic performance by 43% in macro-F1 on average across all visual domains as compared to other critic-free training methods like GRPO. Furthermore, with QoQ-Med trained on intensive segmentation data, it is able to highlight salient regions related to the diagnosis, with an IoU 10x higher than open models while reaching the performance of OpenAI o4-mini. To foster reproducibility and downstream research, we release (i) the full model weights, (ii) the modular training pipeline, and (iii) all intermediate reasoning traces at https://github.com/DDVD233/QoQ_Med.

Immune checkpoint inhibitors (ICIs) and targeted therapies have revolutionized the management of metastatic renal cell carcinoma (mRCC). Currently, the frontline standard of care for patients with mRCC involves the provision of systemic ICI-based combination therapy with no clear guidelines on holding or de-escalating treatment, even with a complete or partial radiological response. Treatments usually continue until disease progression or unacceptable toxicity, frequently leading to overtreatment, which can elevate the risk of toxicity without providing a corresponding increase in therapeutic efficacy. In addition, the ongoing use of expensive antineoplastic drugs increases the financial burden on the already overstretched health care systems and on patients and their families. De-escalation strategies could be designed by integrating contemporary technologies, such as circulating tumor DNA, and advanced imaging techniques, such as computed tomography (CT) scans, positron emission tomography CT, magnetic resonance imaging, and machine learning models. Treatment de-escalation, when appropriate, can minimize treatment-related toxicities, reduce health care costs, and optimize the patients' quality of life while maintaining effective cancer control. This paper discusses the advantages, challenges, and clinical implications of de-escalation strategies in the management of mRCC. PATIENT SUMMARY: In this report, we describe the burden of overtreatment in patients who are never able to stop treatments for metastatic kidney cancer. We discuss the application of the latest technology that can help in making de-escalation decisions.

Recent advances in deep learning-based medical image registration have shown that training deep neural networks~(DNNs) does not necessarily require medical images. Previous work showed that DNNs trained on randomly generated images with carefully designed noise and contrast properties can still generalize well to unseen medical data. Building on this insight, we propose using registration between random images as a proxy task for pretraining a foundation model for image registration. Empirical results show that our pretraining strategy improves registration accuracy, reduces the amount of domain-specific data needed to achieve competitive performance, and accelerates convergence during downstream training, thereby enhancing computational efficiency.

Medical imaging is critical for diagnostics, but clinical adoption of advanced AI-driven imaging faces challenges due to patient variability, image artifacts, and limited model generalization. While deep learning has transformed image analysis, 3D medical imaging still suffers from data scarcity and inconsistencies due to acquisition protocols, scanner differences, and patient motion. Traditional augmentation uses a single pipeline for all transformations, disregarding the unique traits of each augmentation and struggling with large data volumes. To address these challenges, we propose a Multi-encoder Augmentation-Aware Learning (MEAL) framework that leverages four distinct augmentation variants processed through dedicated encoders. Three fusion strategies such as concatenation (CC), fusion layer (FL), and adaptive controller block (BD) are integrated to build multi-encoder models that combine augmentation-specific features before decoding. MEAL-BD uniquely preserves augmentation-aware representations, enabling robust, protocol-invariant feature learning. As demonstrated in a Computed Tomography (CT)-to-T1-weighted Magnetic Resonance Imaging (MRI) translation study, MEAL-BD consistently achieved the best performance on both unseen- and predefined-test data. On both geometric transformations (like rotations and flips) and non-augmented inputs, MEAL-BD outperformed other competing methods, achieving higher mean peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) scores. These results establish MEAL as a reliable framework for preserving structural fidelity and generalizing across clinically relevant variability. By reframing augmentation as a source of diverse, generalizable features, MEAL supports robust, protocol-invariant learning, advancing clinically reliable medical imaging solutions.

The U-shaped encoder-decoder architecture with skip connections has become a prevailing paradigm in medical image segmentation due to its simplicity and effectiveness. While many recent works aim to improve this framework by designing more powerful encoders and decoders, employing advanced convolutional neural networks (CNNs) for local feature extraction, Transformers or state space models (SSMs) such as Mamba for global context modeling, or hybrid combinations of both, these methods often struggle to fully utilize pretrained vision backbones (e.g., ResNet, ViT, VMamba) due to structural mismatches. To bridge this gap, we introduce ACM-UNet, a general-purpose segmentation framework that retains a simple UNet-like design while effectively incorporating pretrained CNNs and Mamba models through a lightweight adapter mechanism. This adapter resolves architectural incompatibilities and enables the model to harness the complementary strengths of CNNs and SSMs-namely, fine-grained local detail extraction and long-range dependency modeling. Additionally, we propose a hierarchical multi-scale wavelet transform module in the decoder to enhance feature fusion and reconstruction fidelity. Extensive experiments on the Synapse and ACDC benchmarks demonstrate that ACM-UNet achieves state-of-the-art performance while remaining computationally efficient. Notably, it reaches 85.12% Dice Score and 13.89mm HD95 on the Synapse dataset with 17.93G FLOPs, showcasing its effectiveness and scalability. Code is available at: https://github.com/zyklcode/ACM-UNet.