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. Recovering true signals from noisy measurements is a central challenge in inverse problems spanning medical imaging, geophysics, and signal processing. Current solutions nearly always balance prior assumptions regarding the true signal (regularization) with agreement to noisy measured data (data-fidelity). Conventional data-fidelity loss functions, such as mean-squared error (MSE) or negative log-likelihood, seek pointwise agreement with noisy measurements, often leading to overfitting to noise. In this work, we instead evaluate data-fidelity collectively by testing whether the observed measurements are statistically consistent with the noise distributions implied by the current estimate.

ICLR 2026 Poster accepted paper at ICLR 2026. Adapters have become a widely adopted strategy for efficient fine-tuning of foundation models, particularly in resource-constrained settings. However, their performance under extreme data scarcity—common in medical imaging due to high annotation costs, privacy regulations, and fragmented datasets—remains underexplored. In this work, we present the first comprehensive study of adapter-based fine-tuning for vision foundation models in low-data medical imaging scenarios. We find that, contrary to their promise, conventional Adapters can degrade performance under severe data constraints, performing even worse than simple linear probing when trained on less than 1\% of the corresponding training data.

ICLR 2026 Poster accepted paper at ICLR 2026. Lightweight 3D medical image segmentation remains constrained by a fundamental "efficiency / robustness conflict", particularly when processing complex anatomical structures and heterogeneous modalities. In this paper, we study how to redesign the framework based on the characteristics of high-dimensional 3D images, and explore data synergy to overcome the fragile representation of lightweight methods. Our approach, VeloxSeg, begins with a deployable and extensible dual-stream CNN-Transformer architecture composed of Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC). For each 3D image, we invoke a "glance-and-focus" principle, where PWA rapidly retrieves multi-scale information, and JLC ensures robust local feature extraction with minimal parameters, significantly enhancing the model's ability to operate with low computational budget. Code/project link: https://github.com/JinPLu/VeloxSeg

ICLR 2026 Poster accepted paper at ICLR 2026. Deep neural networks for medical image segmentation often produce overconfident predictions, posing clinical risks due to miscalibrated uncertainty estimates. In this work, we rethink model calibration from a frequency-domain perspective and identify two critical factors causing miscalibration: spectral bias, where models overemphasize low-frequency components, and confidence saturation, which suppresses overall power spectral density in confidence maps. To address these challenges, we propose a novel frequency-aware calibration framework integrating spectral entropy regularization and power spectral smoothing. The spectral entropy term promotes a balanced frequency spectrum and enhances overall spectral power, enabling better modeling of high-frequency boundary and low-frequency structural uncertainty.

ICLR 2026 Poster accepted paper at ICLR 2026. Generalist multimodal large language models (MLLMs) have achieved impressive performance across a wide range of vision-language tasks. However, their performance on medical tasks—particularly in zero-shot settings where generalization is critical—remains suboptimal. A key research gap is the limited understanding of why medical MLLMs underperform in medical image interpretation. **In this work**, we present a pioneering systematic investigation into the visual grounding capabilities of state-of-the-art medical MLLMs. To disentangle *visual grounding* from *semantic grounding*, we design VGMED, a novel evaluation dataset developed with expert clinical guidance, explicitly assessing the visual grounding capability of medical MLLMs. Code/project link: https://guimeng-leo-liu.github.io/Medical-MLLMs-Fail/

ICLR 2026 Poster accepted paper at ICLR 2026. In clinical applications, the utility of segmentation models is often based on the accuracy of derived downstream metrics such as organ size, rather than by the pixel-level accuracy of the segmentation masks themselves. Thus, uncertainty quantification for such metrics is crucial for decision-making. Conformal prediction (CP) is a popular framework to derive such principled uncertainty guarantees, but applying CP naively to the final scalar metric is inefficient because it treats the complex, non-linear segmentation-to-metric pipeline as a black box. We introduce COMPASS, a practical framework that generates efficient, metric-based CP intervals for image segmentation models by leveraging the inductive biases of their underlying deep neural networks.

ICLR 2026 Poster accepted paper at ICLR 2026. Vision-language models for medical image segmentation often produce masks that conflict with the accompanying text, especially under multi-site/multi-lesion descriptions. We trace this failure to two factors: (i) highly templated and repetitive clinical language causes one-to-one hard contrastive learning to yield numerous false negatives, weakening cross-modal alignment; and (ii) predominantly vision-driven, one-way cross-attention lacks a language-dominant, spatially aware pathway, hindering effective injection of textual semantics into the spatial visual domain. To this end, we propose Consistency-enhanced Two-stage Segmentation (C2Seg). In the pretraining stage, Cluster-aware Contrastive Learning uses a frozen strong baseline to construct an intra-batch text similarity matrix as soft labels, thereby alleviating false negative conflicts and producing more discriminative visual representations.

ICLR 2026 Poster accepted paper at ICLR 2026. Class-incremental learning (CIL) in medical image-guided diagnosis requires models to preserve knowledge of historical disease classes while adapting to emerging categories. Pre-trained models (PTMs) with well-generalized features provide a strong foundation, yet most PTM-based CIL strategies, such as prompt tuning, task-specific adapters and model mixtures, rely on increasingly complex designs. While effective in general-domain benchmarks, these methods falter in medical imaging, where low intra-class variability and high inter-domain shifts (from scanners, protocols and institutions) make CIL particularly prone to representation collapse and domain misalignment. Under such conditions, we find that lightweight representation calibration strategies, often dismissed in general-domain CIL for their modest gains, can be remarkably effective for adapting PTMs in medical settings.

ICLR 2026 Poster accepted paper at ICLR 2026. Recent advances in image-text pretraining have significantly enhanced visual understanding by aligning visual and textual representations. Contrastive Language-Image Pretraining (CLIP) has played a pivotal role in multimodal learning. However, its focus on single-label, single-granularity alignment limits its effectiveness in complex domains such as medical imaging, where images often correspond to multiple labels across different levels of granularity. To address this, we propose Multi-Granular Language Learning (MGLL), a contrastive learning framework designed to improve both multi-label and cross-granularity alignment. Code/project link: https://github.com/HUANGLIZI/MGLL

ICLR 2026 Poster accepted paper at ICLR 2026. Interactive segmentation uses real-time user inputs, such as mouse clicks, to iteratively refine model predictions. Although not originally designed to address distribution shifts, this paradigm naturally lends itself to such challenges. In medical imaging, where distribution shifts are common, interactive methods can use user inputs to guide models towards improved predictions. Moreover, once a model is deployed, user corrections can be used to adapt the network parameters to the new data distribution, mitigating distribution shift. Based on these insights, we aim to develop a practical, effective method for improving the adaptive capabilities of interactive segmentation models to new data distributions in medical imaging. Code/project link: https://github.com/WenTXuL/OAIMS

ICLR 2026 Poster accepted paper at ICLR 2026. Diffusion models have recently demonstrated significant robustness in medical image segmentation, effectively accommodating variations across different imaging styles. However, their applications remain limited due to: (i) current successes being primarily confined to 2D segmentation tasks—we observe that diffusion models tend to collapse at the early stage when applied to 3D medical tasks; and (ii) the inherently isolated iteration along timesteps during training and inference. To tackle these limitations, we propose a novel framework named Cross-Timestep, which incorporates two key innovations: an Adaptive Priori Decoding Strategy (APDS) and a trans-temporal memory LSTM (tLSTM) mechanism. (i) The APDS provides prior guidance during the diffusion process by employing a Priori Decoder(PD) that focuses solely on the conditional branch, successfully stabilizing the reverse diffusion process.

ICLR 2026 Poster accepted paper at ICLR 2026. Self-supervised pre-training has emerged as a critical paradigm for learning transferable representations from unlabeled medical volumetric data. Masked autoencoder based methods have garnered significant attention, yet their application to volumetric medical image faces fundamental limitations from the discrete voxel-level reconstruction objective, which neglects comprehensive anatomical structure continuity. To address this challenge, We propose MedGMAE, a novel framework that replaces traditional voxel reconstruction with 3D Gaussian primitives reconstruction as new perspectives on representation learning. Our approach learns to predict complete sets of 3D Gaussian parameters as semantic abstractions to represent the entire 3D volume, from sparse visible image patches. Code/project link: https://github.com/windrise/MedGMAE; https://anonymous.4open.science/r/MedGMAE-EC8F/

ICLR 2026 Poster accepted paper at ICLR 2026. Visual Question Answering (VQA) enables targeted and context-dependent analysis of medical images, such as chest X-rays (CXRs). However, existing VQA datasets for CXRs are typically constrained by simplistic and brief answer formats, lacking localization annotations (e.g., bounding boxes) and structured tags (e.g., region or radiological finding/disease tags). To address these limitations, we introduce MIMIC-Ext-CXR-QBA (abbr. CXR-QBA), a large-scale CXR VQA dataset derived from MIMIC-CXR, comprising 42 million QA-pairs with multi-granular, multi-part answers, detailed bounding boxes, and structured tags. Code/project link: https://github.com/philip-mueller/mimic-ext-cxr-qba/

ICLR 2026 Poster accepted paper at ICLR 2026. Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models by encouraging step-by-step intermediate reasoning, and recent advances have extended this paradigm to Multimodal Large Language Models (MLLMs). In the medical domain, where diagnostic decisions depend on nuanced visual cues and sequential reasoning, CoT aligns naturally with clinical thinking processes. However, current benchmarks for medical image understanding generally focus on the final answer while ignoring the reasoning path. An opaque process lacks reliable bases for judgment, making it difficult to assist doctors in diagnosis.

ICLR 2026 Poster accepted paper at ICLR 2026. Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to individual tasks, modalities, or organs. This fragmentation contrasts sharply with clinical practice, where experts seamlessly integrate diverse knowledge: anatomical priors from training, exemplar-based reasoning from reference cases, and iterative refinement through real-time interaction. We present $\textbf{K-Prism}$, a unified segmentation framework that mirrors this clinical flexibility by systematically integrating three knowledge paradigms: (i) $\textit{semantic priors}$ learned from annotated datasets, (ii) $\textit{in-context knowledge}$ from few-shot reference examples, and (iii) $\textit{interactive feedback}$ from user inputs like clicks or scribbles. Code/project link: https://github.com/bangwayne/K-Prism

ICLR 2026 Poster accepted paper at ICLR 2026. Generating high-resolution 3D CT volumes with fine details remains challenging due to substantial computational demands and optimization difficulties inherent to existing generative models. In this paper, we propose the Pixel-Level Residual Diffusion Transformer (PRDiT), a scalable generative framework that synthesizes high-quality 3D medical volumes directly at voxel-level. PRDiT introduces a two-stage training architecture comprising 1) a local denoiser in the form of an MLP-based blind estimator operating on overlapping 3D patches to separate low-frequency structures efficiently, and 2) a global residual diffusion transformer employing memory-efficient attention to model and refine high-frequency residuals across entire volumes. This coarse-to-fine modeling strategy simplifies optimization, enhances training stability, and effectively preserves subtle structures without the limitations of an autoencoder bottleneck.

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. Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment. While recent generative approaches have attempted to model progression, key mismatches remain: disease dynamics are inherently continuous and monotonic, yet latent representations are often scattered, lacking semantic structure, and diffusion-based models disrupt continuity through the random denoising process. In this work, we propose treating disease dynamics as a velocity field and leveraging Flow Matching (FM) to align the temporal evolution of patient data. Unlike prior methods, our approach captures the intrinsic dynamics of disease, making progression more interpretable.

ICLR 2026 Poster accepted paper at ICLR 2026. Multimodal large language models are promising for clinical visual question answering tasks, but scaling to 3D imaging is hindered by high computational costs. Prior methods often rely on 2D slices or fixed-length token compression, disrupting volumetric continuity and obscuring subtle findings. We present Photon, a framework that represents 3D medical volumes with token sequences of variable length. Photon introduces instruction-conditioned token scheduling and surrogate gradient propagation to adaptively reduce tokens during both training and inference, which lowers computational cost while mitigating the attention dilution caused by redundant tokens.

ICLR 2026 Poster accepted paper at ICLR 2026. Recent generative pre-trained vision–language (GPTv) models have achieved remarkable success in multi-modal understanding, inspiring their adaptation to medical imaging tasks such as disease diagnosis and visual question answering (VQA). However, current instruction-tuned GPTv models suffer from two key challenges: (1) medical attributes (e.g., disease names, severity grades) are encoded as plain text tokens, collapsing semantically distinct concepts into nearly identical textual sequences; and (2) inadequate textual supervision weakens visual representation learning, leading to severe inter-attribute confusion and misaligned vision–language embeddings. To address these limitations, we introduce attribute tokens (AttTok), a set of pre‑defined special tokens that uniquely encode clinical attributes (e.g., imaging modality, diagnosis, severity) within a structured token space. Complemented by attribute‑centric embedding books, AttTok serves as anchor points for aligning both visual and textual modalities into a shared, discriminative representation space.

ICLR 2026 poster introducing AttTok, a medical vision-language method that uses predefined attribute tokens and attribute-centric mechanisms to improve medical image understanding, including classification and visual question answering.

系统研究医学 MLLM 在医学图像视觉定位中的失效模式，提出 VGMED 评估数据集与 VGRefine 推理时方法，面向医学视觉问答与医学图像解释场景。

一种面向多源异构标注数据的监督训练方法，用于训练单一高性能 AI 基础模型，避免人工标签整合。

MedSAM adapts the Segment Anything paradigm to medical image segmentation and reports broad evaluation across imaging modalities.

AMOS is an abdominal multi-organ segmentation benchmark with CT and MRI cases for evaluating versatile medical image segmentation models. It supports abdominal organ segmentation, modality-general segmentation, and benchmarking of robust 3D segmentation methods.

MedMNIST v2 is a standardized collection of lightweight biomedical image classification datasets, including 2D and 3D tasks. It is useful for quick benchmarking, AutoML, foundation model sanity checks, and reproducible evaluation across multiple medical imaging domains.

SLAKE is a semantically labeled medical visual question answering dataset with bilingual English-Chinese questions, medical images, and knowledge-enhanced annotations. It is useful for medical multimodal learning, image-grounded QA, and radiology VQA evaluation.

Legacy multi-task biomedical image segmentation benchmark retained as a reference; newer segmentation benchmarks are listed above it.

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

Google Health AI Developer Foundations open model resources for medical text and medical image understanding, including MedGemma 1.5 resources.

Interactive medical image segmentation benchmark and baseline from CVPR 2025, covering multiple modalities, organs, and target structures.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Recognition of Anomalies in low-pREvalence cancer Start date: 2026-05-01. End/deadline date: 2026-09-08.

MICCAI Student Board educational challenge for tutorial-style submissions around medical image computing education.

This MDPI AI special issue calls for work on adversarial learning and its applications in healthcare, including robustness, privacy attacks and defenses, federated learning, generative AI for healthcare, and medical image analysis. The page lists a manuscript submission deadline of 2026-10-27.

IEEE BIBM 2026 covers bioinformatics, biomedicine, and health informatics, including machine learning and AI, biomedical image analysis, biomedical signal analysis, clinical decision support, EHR standards, healthcare knowledge representation, NLP and text mining, and precision medicine. The official CFP lists electronic submission of full papers due 2026-07-05, notification on 2026-09-25, camera-ready on 2026-10-25, and the conference on 2026-12-01 to 2026-12-04 in Dallas.