A transformer-based knowledge distillation network for cortical cataract grading
Cortical cataract, a common type of cataract, is particularly difficult to be diagnosed
automatically due to the complex features of the lesions. Recently, many methods based on …
automatically due to the complex features of the lesions. Recently, many methods based on …
Kidney Tumor Classification on CT images using Self-supervised Learning
E Özbay, FA Özbay, FS Gharehchopogh - Computers in Biology and …, 2024 - Elsevier
One of the most common diseases affecting society around the world is kidney tumor. The
risk of kidney disease increases due to reasons such as consumption of ready-made food …
risk of kidney disease increases due to reasons such as consumption of ready-made food …
Comprehensive learning and adaptive teaching: Distilling multi-modal knowledge for pathological glioma grading
The fusion of multi-modal data, eg, pathology slides and genomic profiles, can provide
complementary information and benefit glioma grading. However, genomic profiles are …
complementary information and benefit glioma grading. However, genomic profiles are …
Multi-View disentanglement-based bidirectional generalized distillation for diagnosis of liver cancers with ultrasound images
B-mode ultrasound (BUS) mainly reflects the tissue structural, morphological, and echo
characteristics of liver tumors, and contrast-enhanced ultrasound (CEUS) offers …
characteristics of liver tumors, and contrast-enhanced ultrasound (CEUS) offers …
[HTML][HTML] Advances in 3D pre-training and downstream tasks: a survey
Recent years have witnessed a signifcant breakthrough in the 3D domain. To track the most
recent advances in the 3D field, in this paper, we provide a comprehensive survey of recent …
recent advances in the 3D field, in this paper, we provide a comprehensive survey of recent …
PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis
M Song, J Wang, Z Yu, J Wang, L Yang, Y Lu, B Li… - Medical Image …, 2024 - Elsevier
The conventional pretraining-and-finetuning paradigm, while effective for common diseases
with ample data, faces challenges in diagnosing data-scarce occupational diseases like …
with ample data, faces challenges in diagnosing data-scarce occupational diseases like …
[HTML][HTML] Latent relation shared learning for endometrial cancer diagnosis with incomplete multi-modality medical images
J Li, L Liao, M Jia, Z Chen, X Liu - Iscience, 2024 - cell.com
Magnetic resonance imaging (MRI), ultrasound (US), and contrast-enhanced ultrasound
(CEUS) can provide different image data about uterus, which have been used in the …
(CEUS) can provide different image data about uterus, which have been used in the …
MHD-Net: Memory-aware Hetero-modal Distillation Network for Thymic Epithelial Tumor Typing with Missing Pathology Modality
H Zhang, J Liu, W Liu, H Chen, Z Yu… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Fusing multi-modal radiology and pathology data with complementary information can
improve the accuracy of tumor typing. However, collecting pathology data is difficult since it …
improve the accuracy of tumor typing. However, collecting pathology data is difficult since it …
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Grading
Recently, multimodal deep learning, which integrates histopathology slides and molecular
biomarkers, has achieved a promising performance in glioma grading. Despite great …
biomarkers, has achieved a promising performance in glioma grading. Despite great …
Accelerated Multi-contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
Abstract To accelerate Magnetic Resonance (MR) imaging procedures, Multi-Contrast MR
Reconstruction (MCMR) has become a prevalent trend that utilizes an easily obtainable …
Reconstruction (MCMR) has become a prevalent trend that utilizes an easily obtainable …