[HTML][HTML] DILF: Differentiable rendering-based multi-view Image–Language Fusion for zero-shot 3D shape understanding
Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …
Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning
In this study, a weighted federated learning approach is proposed for electrocardiogram
(ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data …
(ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data …
Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network
Segmenting the liver and liver tumors in computed tomography (CT) images is an important
step toward quantifiable biomarkers for a computer-aided decision-making system and …
step toward quantifiable biomarkers for a computer-aided decision-making system and …
Reviewing 3D convolutional neural network approaches for medical image segmentation
Abstract Background Convolutional neural networks (CNNs) assume pivotal roles in aiding
clinicians in diagnosis and treatment decisions. The rapid evolution of imaging technology …
clinicians in diagnosis and treatment decisions. The rapid evolution of imaging technology …
Heterogeneous transfer learning: recent developments, applications, and challenges
Transfer learning (TL) has emerged as a promising area of research in machine learning
(ML) due to its ability to enhance learning efficiency and accuracy by leveraging knowledge …
(ML) due to its ability to enhance learning efficiency and accuracy by leveraging knowledge …
Embedding tasks into the latent space: cross-space consistency for multi-dimensional analysis in echocardiography
Multi-dimensional analysis in echocardiography has attracted attention due to its potential
for clinical indices quantification and computer-aided diagnosis. It can utilize various …
for clinical indices quantification and computer-aided diagnosis. It can utilize various …
Deep learning-based classification of abrasion and ischemic diabetic foot sores using camera-captured images
Diabetic foot sores (DFS) are serious diabetic complications. The patient's weakened
neurological system damages the tissues of the foot's skin, which results in amputation. This …
neurological system damages the tissues of the foot's skin, which results in amputation. This …
Meta-molnet: A cross-domain benchmark for few examples drug discovery
Predicting the pharmacological activity, toxicity, and pharmacokinetic properties of
molecules is a central task in drug discovery. Existing machine learning methods are …
molecules is a central task in drug discovery. Existing machine learning methods are …
MD-UNet: a medical image segmentation network based on mixed depthwise convolution
Y Liu, S Yao, X Wang, J Chen, X Li - Medical & Biological Engineering & …, 2024 - Springer
In the process of cancer diagnosis and treatment, accurate extraction of the lesion area
helps the doctor to judge the condition. Currently, medical image segmentation algorithms …
helps the doctor to judge the condition. Currently, medical image segmentation algorithms …
SegLD: Achieving universal, zero-shot and open-vocabulary segmentation through multimodal fusion via latent diffusion processes
Open-vocabulary learning can identify categories marked during training (seen categories)
and generalize to categories not annotated in the training set (unseen categories). It could …
and generalize to categories not annotated in the training set (unseen categories). It could …