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Found 9 papers, 3 papers with code
Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
Specifically, we address the clinical reasoning for disease diagnosis, where the LLM generates diagnostic rationales providing its insight on presented patient data and the reasoning path towards the diagnosis, namely Clinical Chain-of-Thought (Clinical CoT).
SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner.
Evidence-empowered Transfer Learning for Alzheimer's Disease
To address this, we present evidence-empowered transfer learning for AD diagnosis.
COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training
In this work, we propose a self-training based unsupervised domain adaptation framework for 3D medical image segmentation named COSMOS and validate it with automatic segmentation of Vestibular Schwannoma (VS) and cochlea on high-resolution T2 Magnetic Resonance Images (MRI).
M3T: Three-Dimensional Medical Image Classifier Using Multi-Plane and Multi-Slice Transformer
The proposed network synergically combines 3D CNN, 2D CNN, and Transformer for accurate AD classification.
Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation
With the advances of deep learning, many medical image segmentation studies achieve human-level performance when in fully supervised condition.
Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI
Joint-ICNet has two main blocks, where one is an MR image reconstruction block that reconstructs an MR image from undersampled multi-coil k-space data and the other is a coil sensitivity maps reconstruction block that estimates coil sensitivity maps from undersampled multi-coil k-space data.
Relevance-CAM: Your Model Already Knows Where To Look
A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of Computer Vision.
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
