Search Results for author:
Found 15 papers, 13 papers with code
ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer
Additionally, we conduct comprehensive experiments on both the generator and classifier, demonstrating the clinical relevance and effectiveness of ProjectedEx in enhancing interpretability and supporting the adoption of AI in medical settings.
A Survey of Medical Vision-and-Language Applications and Their Techniques
Unlike general vision-and-language models trained on diverse, non-specialized datasets, MVLMs are purpose-built for the medical domain, automatically extracting and interpreting critical information from medical images and textual reports to support clinical decision-making.
AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis
This paper takes an unconventional approach by re-examining the CBM framework through the lens of its geometrical representation as a simple linear classification system.
Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis
To address this, we introduce UNet Structured Transformer (UNest), a novel architecture incorporating structural inductive biases for unpaired medical image synthesis.
JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders.
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?
Acknowledging this limitation, our objective is to devise a framework capable of concurrently augmenting medical image and text data.
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability.
Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework
Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease.
SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy.
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors.
Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation
Extensive experiments demonstrate that MaskGAN outperforms state-of-the-art synthesis methods on a challenging pediatric dataset, where MR and CT scans are heavily misaligned due to rapid growth in children.
Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images
Next, we distill the knowledge from the teacher TVUS POD obliteration detector to train the student MRI model by minimizing a regression loss that approximates the output of the student to the teacher using unpaired TVUS and MRI data.
Act Like a Radiologist: Radiology Report Generation across Anatomical Regions
To address these issues, we propose X-RGen, a radiologist-minded report generation framework across six anatomical regions.
Ranked #1 on
Medical Report Generation
on IU X-Ray
(using extra training data)
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging
Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any lesion changes.
