From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification
By Yifan Zhang, Hanbo Chen, Ying Wei, Peilin Zhao, Jiezhang Cao, Xinjuan Fan, X. Lou, Hailing Liu, Jinlong Hou, Xiao Han and 4 more. 2019
70 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Biophysics
In Computer Vision and Pattern Recognition
Citations in the corpus
- Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation (2022, methodology): “Step 6 [50,51,52,53,54,55,56,57,58,59] Flowers, Lamp Shade, Spoon, Candles, Clipboards Scissors, TV, Curtains, Fork, Soda”
- Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution (2020, unknown): “44, 6] and many other applications [7, 50, 52, 11, 20].”
- Dense Regression Network for Video Grounding (2020, background): “Recently, great progress has been achieved in deep learning [48, 47, 2, 15, 13, 14, 19, 1, 51], which facilitates the development of video grounding.”
- NAT: Neural Architecture Transformer for Accurate and Compact Architectures (2019, background): “Deep neural networks (DNNs) [25] have been producing state-of-the-art results in many challenging tasks including image classification [12, 23, 42, 57, 58, 18, 11, 53], face recognition [38, 43, 56], brain signal processing [33, 34], video analysis [50, 49] and many other areas [55, 54, 24, 3, 10, 9”
- Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (2021, background): “The key challenge is the distribution discrepancies among domains, leading to poor performance of neural networks on the target domain [71, 74].”