搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. The ability to distinguish subtle differences between visually similar images is essential for diverse domains such as industrial anomaly detection, medical imaging, and aerial surveillance. While comparative reasoning benchmarks for vision-language models (VLMs) have recently emerged, they primarily focus on images with large, salient differences and fail to capture the nuanced reasoning required for real-world applications. In this work, we introduce **VLM-SubtleBench**, a benchmark designed to evaluate VLMs on *subtle comparative reasoning*. Our benchmark covers ten difference types—Attribute, State, Emotion, Temporal, Spatial, Existence, Quantity, Quality, Viewpoint, and Action—and curate paired question–image sets reflecting these fine-grained variations.

ICLR 2026 Poster accepted paper at ICLR 2026. Anomaly detection is a critical task in computer vision with profound implications for medical imaging, where identifying pathologies early can directly impact patient outcomes. While recent unsupervised anomaly detection approaches show promise, they require substantial normal training data and struggle to generalize across anatomical contexts. We introduce D$^2$4FAD, a novel dual distillation framework for few-shot anomaly detection that identifies anomalies in previously unseen tasks using only a small number of normal reference images. Our approach leverages a pre-trained encoder as a teacher network to extract multi-scale features from both support and query images, while a student decoder learns to distill knowledge from the teacher on query images and self-distill on support images. Code/project link: https://github.com/ttttqz/D24FAD

ICLR 2026 Poster accepted paper at ICLR 2026. Accurate cell instance segmentation is foundational for digital pathology analysis. Existing methods based on contour detection and distance mapping still face significant challenges in processing complex and dense cellular regions. Graph coloring-based methods provide a new paradigm for this task, yet the effectiveness of this paradigm in real-world scenarios with dense overlaps and complex topologies has not been verified. Addressing this issue, we release a large-scale dataset GBC-FS 2025, which contains highly complex and dense sub-cellular nuclear arrangements. We conduct the first systematic analysis of the chromatic properties of cell adjacency graphs across four diverse datasets and reveal an important discovery: most real-world cell graphs are non-bipartite, with a high prevalence of odd-length cycles (predominantly triangles).

ICLR 2026 Poster accepted paper at ICLR 2026. Learning from multi-variate time-series with heterogeneous channel configurations remains a fundamental challenge for deep neural networks, particularly in clinical domains such as intracranial electroencephalography (iEEG), where channel setups vary widely across subjects. In this work, we introduce multi-variate parallel attention (MVPA), a novel self-attention mechanism that disentangles content, temporal, and spatial attention, enabling flexible, generalizable, and efficient modeling of time-series data with varying channel counts and configurations. We use MVPA to build MVPFormer, a generative foundation model for human electrophysiology, trained to predict the evolution of iEEG signals across diverse subjects. To support this and future efforts by the community, we release the SWEC iEEG dataset, the largest publicly available iEEG dataset to date, comprising nearly 10,000 hours of recordings from heterogeneous clinical sources. Code/project link: https://github.com/IBM/multi-variate-parallel-transformer; https://huggingface.co/datasets/NeuroTec/SWEC_iEEG_Dataset

ICLR 2026 Poster accepted paper at ICLR 2026. Surgical triplet detection is a critical task in surgical video analysis, with significant implications for performance assessment and training novice surgeons. However, existing datasets like CholecT50 lack precise spatial bounding box annotations, rendering triplet classification at the image level insufficient for practical applications. The inclusion of bounding box annotations is essential to make this task meaningful, as they provide the spatial context necessary for accurate analysis and improved model generalizability. To address these shortcomings, we introduce ProstaTD, a large-scale, multi-institutional dataset for surgical triplet detection, developed from the technically demanding domain of robot-assisted prostatectomy.

ICLR 2026 Poster accepted paper at ICLR 2026. Healthcare insurance fraud detection presents unique machine learning challenges: labeled data are scarce due to delayed verification processes, and fraudulent behaviors evolve rapidly, often manifesting in complex, graph-structured interactions. Existing methods struggle in such settings. Pretraining routines typically overlook structural anomalies under limited supervision, while online models often fail to adapt to changing fraud patterns without labeled updates. To address these issues, we propose the Continual Fiedler Vector Graph model (ConFVG), a fraud detection framework designed for label-scarce and non-stationary environments.

ICLR 2026 Poster accepted paper at ICLR 2026. Accurate detection of clinically meaningful events in healthcare time-series data is crucial for reliable downstream analysis and decision support. However, most existing methods struggle to jointly localize event boundaries and classify event types; even detection transformer (DETR)-based approaches show limited performance when confronted with extremely sparse events typical of clinical recordings. To address these challenges, we propose a coarse-to-fine detection framework combining a global context explorer, a local detail inspector, and an adaptive gating module (AGM) that fuses multiple label perspectives. The AGM uses transformed labels—encoding event presence and temporal position—to improve learning on sparse events.

ICLR 2026 Poster accepted paper at ICLR 2026. Interpreting clinical electroencephalography (EEG) is a laborious, subjective process, and existing computational models are limited to narrow classification tasks rather than holistic interpretation. A key bottleneck for applying powerful Large Vision-Language Models (LVLMs) to this domain is the scarcity of datasets pairing EEG visualizations with fine-grained, expert-level annotations. We address this by introducing CerebraGloss, an instruction-tuned LVLM for nuanced EEG interpretation. We first introduce a novel, automated data generation pipeline, featuring a bespoke YOLO-based waveform detector, to programmatically create a large-scale corpus of EEG-text instruction data. Code/project link: https://github.com/iewug/CerebraGloss

ICLR 2026 Poster accepted paper at ICLR 2026. Accurate detection of all pathological findings in 3D medical images remains a significant challenge, as supervised models are limited to detecting only the few pathology classes annotated in existing datasets. To address this, we frame pathology detection as an unsupervised visual anomaly segmentation (UVAS) problem, leveraging the inherent rarity of pathological patterns compared to healthy ones. We enhance the existing density-based UVAS framework with two key innovations: (1) dense self-supervised learning for feature extraction, eliminating the need for supervised pretraining, and (2) learned, masking-invariant dense features as conditioning variables, replacing hand-crafted positional encodings. Trained on over 30,000 unlabeled 3D CT volumes, our fully self-supervised model, Screener, outperforms existing UVAS methods on four large-scale test datasets comprising 1,820 scans with diverse pathologies. Code/project link: https://github.com/mishgon/screener; https://anonymous.4open.science/r/screener-35EE/

ICLR 2026 Poster accepted paper at ICLR 2026. Body composition analysis through CT and MRI imaging provides critical insights for cardio-metabolic health assessment but remains limited by accessibility barriers including radiation exposure, high costs, and infrastructure requirements. We present AbdCTBench, a large-scale dataset containing 23,506 CT-derived abdominal surface meshes from 18,719 patients, paired with 87 comorbidity labels, 31 specific diagnosis codes, and 16 CT-derived biomarkers. Our key insight is that external surface geometry is predictive of internal tissue composition, enabling accessible health screening through consumer devices. We establish comprehensive benchmarks across seven computer vision architectures (ResNet-18/34/50, DenseNet-121, EfficientNet-B0, ViT-Small, Swin Transformer-Base), demonstrating that models can learn robust surface-to-biomarker representations directly from 2D mesh projections. Code/project link: https://abdctbenchrepo.github.io/AbdCTBench/

The RSNA Pneumonia Detection Challenge dataset is a chest radiograph benchmark for detecting pneumonia-related lung opacities. It supports object detection, chest X-ray classification, localization, and radiology AI evaluation under a competition framework.

MURA is a musculoskeletal radiograph dataset from Stanford for abnormality detection in upper extremity X-rays. It is used for radiology classification, fracture-related screening, musculoskeletal imaging AI, and human-AI comparison studies.

CAMELYON17 is a digital pathology dataset for detecting breast cancer metastases in lymph node whole-slide images across multiple centers. It supports pathology classification, metastasis detection, weakly supervised learning, and domain generalization in histopathology AI.

The ISIC Archive is a large public dermatology image repository for skin lesion analysis. It is widely used for melanoma classification, lesion segmentation, dermoscopic image retrieval, bias and domain shift analysis, and clinical imaging benchmark development.

LIDC-IDRI is a lung CT dataset with thoracic CT scans and expert nodule annotations. It is a classic benchmark for lung nodule detection, segmentation, malignancy characterization, radiomics, and computer-aided diagnosis research.

VinDr-CXR is a chest X-ray dataset with radiologist annotations from Vietnamese hospitals. It supports abnormality classification, lesion localization, radiology object detection, and robustness studies across clinical sites and populations.

NIH ChestX-ray14 is a public chest radiograph dataset with image-level labels for thoracic disease findings mined from reports. It is commonly used for chest X-ray classification, weak supervision, thoracic disease detection, and radiology benchmark comparisons.

PTB-XL is a large public 12-lead electrocardiography dataset with diagnostic statements and waveform records. It is a standard benchmark for ECG classification, cardiac abnormality detection, clinical signal representation learning, and robust evaluation of biosignal models.

MIMIC-IV-ECG is a large deidentified electrocardiogram dataset linked to the MIMIC-IV clinical data ecosystem. It supports ECG classification, arrhythmia detection, representation learning, and multimodal modeling with structured EHR context.

NIH Clinical Center chest X-ray dataset released for computer-aided detection and radiology machine learning research.

Foundation model and toolkit for all-in-one biomedical image parsing across recognition, detection, and segmentation tasks.

京东健康全球医疗 AI 创新大赛公开页面显示赛事聚焦睡眠监测智能算法与医疗大模型创新应用两个方向，面向全球高校、科研机构、企业和个人开放报名，赛程含 6.17-8.7 初赛、后续复赛和 9.21 决赛。

This Nature Portfolio / npj Gut and Liver collection is open for submissions until 2026-05-12. It welcomes work on risk assessment and early detection of gastrointestinal and liver cancers, including artificial intelligence tools for cancer risk assessment, early-stage detection, novel imaging, tissue acquisition modalities, and health economics for screening.

This Nature Portfolio collection is open for submissions until 2026-05-15. It focuses on AI technologies for population medicine and public health, including infectious-disease early warning, pathogen detection, chronic disease risk stratification, policy simulation, wearable AI, multimodal fusion, federated learning, privacy preservation, and foundation models.

This Technologies special issue calls for work on AI-enabled smart healthcare systems. It is relevant to medical AI submissions on intelligent monitoring, anomaly detection, assistive technologies, smart sensing, clinical decision support, and AI-assisted healthcare workflows. The page lists a manuscript submission deadline of 2026-08-30.

This Taylor & Francis / Risk Management and Healthcare Policy article collection focuses on artificial intelligence in healthcare risk management, including AI applications for early disease detection, personalized treatment optimization, real-time risk prediction, patient safety, and health-system risk management. The CFP listing gives a 2026-05-31 submission deadline.