Generating and weighting semantically consistent sample pairs for ultrasound contrastive learning
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power
in extracting lesion-related features. Building such large and well-designed medical …
in extracting lesion-related features. Building such large and well-designed medical …
Learning from ambiguous labels for lung nodule malignancy prediction
Lung nodule malignancy prediction is an essential step in the early diagnosis of lung
cancer. Besides the difficulties commonly discussed, the challenges of this task also come …
cancer. Besides the difficulties commonly discussed, the challenges of this task also come …
Strided self-supervised low-dose CT denoising for lung nodule classification
Lung nodule classification based on low-dose computed tomography (LDCT) images has
attracted major attention thanks to the reduced radiation dose and its potential for early …
attracted major attention thanks to the reduced radiation dose and its potential for early …
Trustworthy learning with (un) sure annotation for lung nodule diagnosis with CT
Recent evolution in deep learning has proven its value for CT-based lung nodule
classification. Most current techniques are intrinsically black-box systems, suffering from two …
classification. Most current techniques are intrinsically black-box systems, suffering from two …
A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based …
The presence of lung metastases in patients with primary malignancies is an important
criterion for treatment management and prognostication. Computed tomography (CT) of the …
criterion for treatment management and prognostication. Computed tomography (CT) of the …
Faithful learning with sure data for lung nodule diagnosis
Recent evolution in deep learning has proven its value for CT-based lung nodule
classification. Most current techniques are intrinsically black-box systems, suffering from two …
classification. Most current techniques are intrinsically black-box systems, suffering from two …