Self-supervised learning with limited labeled data for prostate cancer detection in high frequency ultrasound
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data
shows great promise for prostate cancer (PCa) detection. Previous approaches to analysis …
shows great promise for prostate cancer (PCa) detection. Previous approaches to analysis …
A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound
Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being
crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) …
crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) …
TRUSformer: Improving prostate cancer detection from micro-ultrasound using attention and self-supervision
Purpose A large body of previous machine learning methods for ultrasound-based prostate
cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within …
cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within …
Training deep networks for prostate cancer diagnosis using coarse histopathological labels
Motivation: Accurate detection of prostate cancer using ultrasound data is a challenging yet
highly relevant clinical question. A significant roadblock for training accurate models for …
highly relevant clinical question. A significant roadblock for training accurate models for …
LensePro: Label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations
Purpose The standard of care for prostate cancer (PCa) diagnosis is the histopathological
analysis of tissue samples obtained via transrectal ultrasound (TRUS) guided biopsy …
analysis of tissue samples obtained via transrectal ultrasound (TRUS) guided biopsy …
Towards targeted ultrasound-guided prostate biopsy by incorporating model and label uncertainty in cancer detection
Purpose Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is
blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false …
blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false …
Training deep neural networks with noisy clinical labels: toward accurate detection of prostate cancer in US data
Purpose: Ultrasound is the standard-of-care to guide the systematic biopsy of the prostate.
During the biopsy procedure, up to 12 biopsy cores are randomly sampled from six zones …
During the biopsy procedure, up to 12 biopsy cores are randomly sampled from six zones …
Overview of the potentials of multiple instance learning in cancer diagnosis: Applications, challenges, and future directions
TPT Armand, S Bhattacharjee… - 2024 26th International …, 2024 - ieeexplore.ieee.org
The outcome of cancer patients mostly depends on the diagnosis process and the treatment
strategies. Computer-aided diagnosis (CAD) methods have demonstrated the potential to …
strategies. Computer-aided diagnosis (CAD) methods have demonstrated the potential to …
Coarse label refinement for improving prostate cancer detection in ultrasound imaging
Purpose: Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection,
yet is limited by a high rate of false negatives and low diagnostic yield of the current …
yet is limited by a high rate of false negatives and low diagnostic yield of the current …
Characterizing the uncertainty of label noise in systematic ultrasound-guided prostate biopsy
Ultrasound imaging is a common tool used in prostate biopsy. The challenges associated
with using a systematic and nontargeted approach are the high rate of false negatives and …
with using a systematic and nontargeted approach are the high rate of false negatives and …