Deep learning for X-ray, CT, MRI, digital pathology, and medical image analysis.
28 papers in this topic (ICML 2026).
Jun Li, Ziwei Qin
Semi-supervised learning has become a dominant paradigm for reducing annotation costs. However, we argue that the current progress is clouded by a twofold overconfidence problem.…
Junda Ying, Yuxuan Wang, Bowen Yang, Peijie Zhou +1
Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and…
Tan Pan, Shuhao Mei, Yixuan Sun, Kaiyu Guo +4
Self-supervised pre-training methods in medical imaging typically treat each individual as an isolated instance, learning representations through augmentation-based objectives or…
Hengrui Lou, Weihan Li, Jiazhen Yang, Lingxiang Jia +4
Computational pathology has made progress in diagnosis and prognosis prediction from whole slide images (WSIs), yet pipelines still rely on patch-level feature extraction and…
Mohammad Hosseini, Eray Erturk, Saba Hashemi, Maryam M. Shanechi
Large-scale, multi-subject widefield calcium imaging provides unprecedented access to brain-wide cortical dynamics. However, the high dimensionality, complex spatiotemporal…
Ivan Svatko, Maxime Sanchez, Ihab Bendidi, Gilles Cottrell +1
Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features. In microscopy imaging, however, it remains…
Yucheng Xing, Ling Huang, Jingying Ma, Ruping Hong +4
Whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling…
Jingbo Yang, Yunfeng Zhao, Chao Qiu, Yulin Sun +2
Stereotactic electroencephalography (sEEG) provides temporally precise intracranial recordings but is inherently constrained by sparse and irregular spatial sampling due to…
Yingyu Chen, Yongqiang Huang, Yang Qin, Ziyuan Yang +3
In clinical practice, patients often present with multiple co-occurring diseases, yet most existing Multi-Label-Diagnosis (MLD) methods treat diagnosis as a rigid discriminative…
Luru Jing, Cong Cong, Yanyuan Chen, Yongzhi Cao
Federated learning (FL) offers a promising framework for collaborative digital pathology by enabling model training across institutions. However, real-world deployments face…
Zheng Zhang, Hao Tang, Yingying Hu, zhanli hu +1
Low-count Positron Emission Tomography (PET) reconstruction is severely hindered by the dissipative nature of prevailing generative models, where the inherent phase-space…
Qi Chen, Shuhan Ding, Yu Gu, Nan Liu +4
Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, training CT-specific VAEs from…
Jialin Li, Zhuo Zhang, Cao Yue, Guipeng Lan +3
The scarcity of high-quality imaging data for coronary angiography (CAG) stenosis limits the clinical translation of automated stenosis detection. Synthetic stenosis data provides…
Ganxi Xu, Zhao-Rong Lai, Yuting Tang, Yonghao Song +4
Visual prostheses hold great promise for restoring vision in blind individuals. While researchers have successfully utilized M/EEG signals to evoke visual perceptions during the…
Salma J. Ahmed, Emad Mohammed, Azam Asilian Bidgoli
Modern segmentation models achieve strong predictive performance but remain largely opaque, limiting our ability to diagnose failures, understand dataset shift, or intervene in a…
Ziyuan Gao
Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires…
Zitao Chen, Jiatong Ji, Yinjun Jia, Wei-Ying Ma +1
Recent advances in 3D molecular modeling have achieved high-fidelity structural synthesis, yet these models often lack an explicit and manipulable representation space. To address…
Chunlei Li, Zixuan Zheng, Yilei Shi, Guanglu Dong +4
Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labels. We address this challenge…
Yankai Jiang, Yujie Zhang, Peng Zhang, Wenjie Li +4
Recent medical MLLMs have made significant progress in generating step by step textual reasoning chains. However, they still struggle with complex clinical tasks that necessitate…
Jiangtao Yan, Yanlin Qu, Yansheng Qiu, Shujian Gao +4
The longitudinal management of blinding fundus diseases constitutes a Partially Observable Markov Decision Process (POMDP) necessitating a critical precision-risk trade-off…
Hao Zhou, Simon A. Lee, Cyrus Tanade, Keum San Chun +4
Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, most existing…
Chenhe Du, Xuanyu Tian, Qing Wu, Muyu Liu +3
Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular…
Haonan Zhang, Qing Wu, Xuanyu Tian, Bowen Li +2
Implicit Neural Representation (INR) has emerged as a powerful paradigm for continuous MRI reconstruction. However, standard self-supervised INR requires time-consuming…
Yipan Wei, Wenke Huang, Yapeng Li, He Li +3
Federated Prompt Learning (FPL) adapts Vision-Language Models to privacy-sensitive medical imaging, typically via a textual tuning paradigm that assumes the frozen visual encoder…
Connor Lane, Mihir Tripathy, Leema Krishna Murali, Ratna Sagari Grandhi +4
We study the problem of training self-supervised foundation models for functional MRI. Our main contributions are: (1) we introduce a new model family (CortexMAE) trained using…
Erjian Zhang, Yatong Hao, Liejun Wang, Zhiqing Guo
While multi-task learning based automatic radiology report generation (RRG) is widely adopted to ensure clinical consistency, most focus on architectural designs yet remain…
Jiusong Ge, Yingkang Zhan, Wenjie Zhao, Di Zhang +4
Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and…
Moritz Schaefer, Zoe Piran, Nils Philipp Walter, Animesh Awasthi +3
The characterization of histopathology with AI promises to assist clinical decision-making, but it is currently limited due to coarse-grained annotations that miss cellular…
Metadata from BioTender-max/icml2026-ai-bio (CC0-1.0).