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LMU researchers developed a Quantum Boltzmann Machine model for pneumonia detection from chest X-rays. Using fewer than 9,000 parameters (vs. millions in classical CNNs), the model achieves 84–86% accuracy, demonstrating potential advantages of quantum machine learning for small medical datasets.
Indian scientists at IIT-Madras have created the most detailed 3D atlas of the human brainstem at cellular resolution, called Anchor, integrating over 500 tissue sections to map more than 200 cell clusters and pathways, bridging whole-brain imaging and cellular pathology.
ConceptSMILE is a perturbation-based auditing framework for evaluating the reliability of concept-based explainable AI, tested on retinal fundus images.
SAGEAgent is an LLM-based clinical agent that sequentially decides which diagnostic modalities to acquire for cancer patients to balance predictive accuracy with clinical invasiveness, reducing acquisition burden by 55% while maintaining competitive survival prediction performance.
Introduces AMID, an autonomous multi-agent framework for medical imaging model development that uses LLM agents to plan, execute, and verify experiments. It outperforms general-purpose MLE systems and approaches human-designed solutions on 20 medical imaging tasks.
This paper studies the fairness problem of thresholded subgroup underdiagnosis in long-tailed chest X-ray classification, demonstrating that rare-label fairness depends jointly on the finding, subgroup, and operating threshold, not on label frequency or ranking metrics alone.
This paper introduces a pipeline that converts free-text chest radiograph reports into multi-label matrices with a single structured annotation pass, enabling reconfiguration of label schemas via dictionary edits without relabeling, saving significant cost and time.
This paper proposes PhyMRI-SR, a physics-aware MRI super-resolution method that uses Gaussian splatting and physics-constrained modeling to dynamically adapt resolution-SNR configurations, achieving state-of-the-art performance.
This paper proposes a token-centric dual-view learning framework that unifies prompt-based adaptation and cross-view fusion within a frozen vision transformer to improve breast cancer classification from mammography images, achieving consistent improvements on VinDr-Mammo and CMMD datasets.
The video showcases a prototype full-body ultrasound scanner developed by the Midjourney team, using 40 modified probes to scan simultaneously in a water tank. It aims to provide low-cost, accessible 3D medical imaging. While not replacing MRI, it has the potential to improve healthcare accessibility.
NeuroBridge is a clinically guided multi-task MRI framework for diagnosing Alzheimer's disease and mild cognitive impairment, achieving high accuracy and cross-cohort generalization.
CONFLUX is a 3D latent diffusion model for chest CT synthesis that achieves high-fidelity volumetric generation with controllable clinical attributes, enhanced by a reinforcement learning post-training stage to improve conditioning reliability. The model and a synthetic dataset of ~200k chest CT volumes are released.
Introduces MRPO, a reinforcement learning algorithm that uses step-wise process rewards to mitigate cascading errors in clinical multimodal reasoning, outperforming existing methods on medical VQA benchmarks.
Introduces a distribution-based multiple instance learning framework using zero-inflated beta modeling to improve tumor proportion score prediction in non-small cell lung cancer from histopathology slides.
This paper presents an automated deep learning approach for brain tumor detection in MRI images using CNN and ResNet architectures with transfer learning, achieving up to 97% accuracy.
Aloe-Vision introduces a family of open medical Vision-Language Models trained on a quality-filtered mixture of medical and general data, along with a new benchmark CareQA-Vision for reliable evaluation. The models demonstrate competitive performance while highlighting vulnerabilities to adversarial inputs.
This paper presents a latent dynamical model using a heart-rate-aware neural ODE and graph-based mesh autoencoder to model full-cycle ventricular motion from cine cardiac MRI. Applied to 72,386 UK Biobank participants, the model improves heart failure risk prediction over conventional cardiac markers.
This paper introduces Prob-BBDM, a probabilistic Brownian Bridge Diffusion Model for efficient and high-quality MRI sequence synthesis from 2D axial slices, achieving up to 88.46% SSIM and 26.09 dB PSNR with only 4 diffusion steps, and demonstrating clinical utility in tumor segmentation.
Midjourney announced a pivot to medical imaging with an ultrasound body scanner, but experts are skeptical due to lack of evidence and the high-stakes nature of medicine.
This paper introduces transition-aware best-of-N sampling, a training-free method for generating longitudinal chest X-ray reports by encoding changes between prior and current examinations using set-to-set distance metrics.