One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer

Documenti analoghi

• Handling Noisy Labels via One-Step Abductive Multi-Target Learning and Its Application to Helicobacter Pylori Segmentation
  di: Yang, Yongquan, et al.
  Pubblicazione: (2020)
• Experts' cognition-driven safe noisy labels learning for precise segmentation of residual tumor in breast cancer
  di: Yang, Yongquan, et al.
  Pubblicazione: (2023)
• Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels: An Application Study on Tumour Segmentation for Breast Cancer
  di: Yang, Yongquan, et al.
  Pubblicazione: (2023)
• Collagen Density Is Associated With Pathological Complete Response to Neoadjuvant Chemotherapy in Triple‐Negative Breast Cancer Patients
  di: Fengling Li, et al.
  Pubblicazione: (2024)
• Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer
  di: Yang, Yongquan, et al.
  Pubblicazione: (2023)