25 citations · 82 across the 21 of their papers we have counts for
6 papers · 1 filter
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation
Wentao Pan, Jiangpeng Yan, Hanbo Chen +4
Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole sli…
Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification
ChengHui Yu, MingKang Tang, ShengGe Yang +7
Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis. However, limited by the signific…
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction
Jiangpeng Yan, Chenghui Yu, Hanbo Chen +4
Recently, deep neural networks have greatly advanced undersampled Magnetic Resonance Image (MRI) reconstruction, wherein most studies follow the one-anatomy-one-network fashion, i.…
UniInst: Unique Representation for End-to-End Instance Segmentation
Yimin Ou, Rui Yang, Lufan Ma +5
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anch…
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Sharib Ali, Noha Ghatwary, Debesh Jha +29
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and charac…
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning
Jiawei Yang, Hanbo Chen, Jiangpeng Yan +2
Few-shot learning is an established topic in natural images for years, but few work is attended to histology images, which is of high clinical value since well-labeled datasets and…