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Works526 from public data
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- A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging517
This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.
- Large language models for generative information extraction: a survey507
This study surveys the most recent advancements in generative Large Language Models efforts for IE tasks and identifies several insights in technique and promising research directions that deserve further exploration in future studies.
- Combo loss: Handling input and output imbalance in multi-organ segmentation459
A new curriculum learning based loss function that leverages Dice similarity coefficient to deter model parameters from being held at bad local minima and at the same time gradually learn better model parameters by penalizing for false positives/negatives using a cross entropy term is introduced.
- Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network423
This work proposes a generic cross-modality synthesis approach and shows that these goals can be achieved with an end-to-end 3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks.
- Morphological diversity of single neurons in molecularly defined cell types417
This study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single- cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.
- BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion354
Three spatial constraints are designed and seamlessly integrated into the denoising step of diffusion models, requiring no additional training and massive annotated layout data, and demonstrate that the proposed constraints can control what and where to present in the images while retaining the ability of Diffusion models to synthesize with high fidelity and diverse concept coverage.
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- Deep Learning and Convolutional Neural Networks for Medical Image Computing289
The impact of deep learning on automated disease detection and organ and lesion segmentation and the remaining knowledge gaps that must be overcome to achieve clinician-level performance of automated medical image processing systems are discussed.
- PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction281
A component to predict potential relations is designed, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity.
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- X2CT-GAN: Reconstructing CT From Biplanar X-Rays With Generative Adversarial Networks276
This work proposes to reconstruct CT from two orthogonal X-rays using the generative adversarial network (GAN) framework, resulting in a high-quality CT volume both visually and quantitatively.
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- A medical multimodal large language model for future pandemics215
A medical multimodal large language model (Med-MLLM) for radiograph representation learning is reported, which can learn broad medical knowledge from unlabelled data and can be used for clinical tasks that involve both visual and textual data.
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- Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation202
This paper comprehensively introduces and benchmark a family of plug-and-play strategies for ambiguous target selection from the perspectives of entropy, model uncertainty and label noise self-identification, respectively, and incorporates the estimated ambiguity map into the consistency loss to encourage consensus between the two models' predictions in these informative regions.
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- When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications196
A novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA, which aims to absorb both the benefits of mixture-of-expert (MOE) for multi-task learning and low-rank adaptation (LoRA) for parameter efficient fine-tuning.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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