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

20 October 2021

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

7 / 7 papers shown

Title
Validation of the Practicability of Logical Assessment Formula for Evaluations with Inaccurate Ground-Truth Labels Yongquan Yang Hong Bu 26 0 0 06 Jul 2023
Experts' cognition-driven ensemble deep learning for external validation of predicting pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer Yongquan Yang Fengling Li Yani Wei Yuanyuan Zhao Jing Fu Xiuli Xiao Hong Bu 33 3 0 19 Jun 2023
Segment Anything Model for Medical Images? Yuhao Huang Yitian Zhao Lei Mou H. Fu Ao Chang ... Lei Li Vicente Grau M. Akiba Fajin Dong Jiang-Dong Liu VLM 80 40 0 28 Apr 2023
Experts' cognition-driven safe noisy labels learning for precise segmentation of residual tumor in breast cancer Yongquan Yang Jie Chen Yani Wei Mohammad H. Alobaidi Hong Bu NoLa 45 1 0 13 Apr 2023
Transformers in Vision: A Survey Salman Khan Muzammal Naseer Munawar Hayat Syed Waqas Zamir F. Khan M. Shah ViT 227 2,430 0 04 Jan 2021
Moderately Supervised Learning: Definition, Framework and Generality Yongquan Yang OffRL 19 3 0 27 Aug 2020
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan Alex Kendall R. Cipolla SSeg 446 15,639 0 02 Nov 2015