Search Results for author:
Found 8 papers, 5 papers with code
RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement
Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time.
Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers
Workplace injuries are common in today's society due to a lack of adequately worn safety equipment.
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection.
PatchDropout: Economizing Vision Transformers Using Patch Dropout
This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size.
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms.
Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT
Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent.
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.
Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models
With this in mind, we trained networks using three different criteria to select the positive training data (i. e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis.
