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Found 11 papers, 3 papers with code
Explainable Transformer Prototypes for Medical Diagnoses
Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions.
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process.
Radiomics Boosts Deep Learning Model for IPMN Classification
We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field.
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles
Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients.
Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation
To this end, we proposed a new cyclic optimization method (\textit{CLMR}) to address the efficiency and accuracy problems in deep learning based medical image segmentation.
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence
We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging.
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning.
Transformer based Generative Adversarial Network for Liver Segmentation
The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
