AIMC Topic: Image Interpretation, Computer-Assisted

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Enhancing Medical Vision-Language Contrastive Learning via Inter-Matching Relation Modeling.

IEEE transactions on medical imaging
Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred ...

FedBCD: Federated Ultrasound Video and Image Joint Learning for Breast Cancer Diagnosis.

IEEE transactions on medical imaging
Ultrasonography plays an essential role in breast cancer diagnosis. Current deep learning based studies train the models on either images or videos in a centralized learning manner, lacking consideration of joint benefits between two different modali...

Hierarchical Multi-Class Group Correlation Learning Network for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Hierarchical approaches have been tremendously successful at multi-label segmentation. However, it has been shown they may seriously suffer from the problem of only imposing constraints on shallow layers while ignoring deep relationships in the label...

Automated Ensemble Multimodal Machine Learning for Healthcare.

IEEE journal of biomedical and health informatics
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. Thi...

TGAP-Net: Twin Graph Attention Pseudo-Label Generation for Weakly Supervised Semantic Segmentation.

IEEE journal of biomedical and health informatics
Multilabel pathological tissue segmentation is a vital task in computational pathology that aims to semantically segment different tissues within pathological images. Fully and weakly supervised models have demonstrated impressive performances in thi...

Modality-Aware Distillation Network for Microvascular Invasion Prediction of Hepatocellar Carcinoma From MRI Images.

IEEE transactions on bio-medical engineering
Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a crucial histopathologic prognostic factor associated with cancer recurrence after liver transplantation or hepatectomy. Recently, clinicoradiologic characteristics are combined with ...

Deep learning-enhanced zero echo time MRI for glenohumeral assessment in shoulder instability: a comparative study with CT.

Skeletal radiology
PURPOSE: To evaluate image quality and lesion conspicuity of zero echo time (ZTE) MRI reconstructed with deep learning (DL)-based algorithm versus conventional reconstruction and to assess DL ZTE performance against CT for bone loss measurements in s...

Deep Learning-Based Automated Measurement of Cervical Length in Transvaginal Ultrasound Images of Pregnant Women.

IEEE journal of biomedical and health informatics
Cervical length (CL) measurement using transvaginal ultrasound is an effective screening tool to assess the risk of preterm birth. An adequate assessment of CL is crucial, however, manual sonographic CL measurement is highly operator-dependent and cu...

GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.

IEEE journal of biomedical and health informatics
A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classifica...

MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework.

IEEE journal of biomedical and health informatics
Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is time-consuming and labor-intensive. Current AI-based methods for automatic structure assessment, ...