47 citations · 88 across the 11 of their papers we have counts for
11 papers
Self-Supervised Scalable Deep Compressed Sensing
Bin Chen, Xuanyu Zhang, Shuai Liu +2
Compressed sensing (CS) is a promising tool for reducing sampling costs. Current deep neural network (NN)-based CS methods face the challenges of collecting labeled measurement-gro…
HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image
Zhuchen Shao, Yang Chen, Hao Bian +3
Survival prediction based on whole slide images (WSIs) is a challenging task for patient-level multiple instance learning (MIL). Due to the vast amount of data for a patient (one o…
Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
Chuang Zhu, Shengjie Liu, Zekuan Yu +15
For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue t…
Progressive Content-aware Coded Hyperspectral Compressive Imaging
Xuanyu Zhang, Bin Chen, Wenzhen Zou +4
Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology. By acquiring 3D hyperspectral images (HSIs) via 2D sensors,…
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Simon Graham, Quoc Dang Vu, Mostafa Jahanifar +86
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovatio…
Accurate Image Restoration with Attention Retractable Transformer
Jiale Zhang, Yulun Zhang, Jinjin Gu +3
Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To…
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Muhammad Hassan, Haifei Guan, Aikaterini Melliou +24
Neural networks have been rapidly expanding in recent years, with novel strategies and applications. However, challenges such as interpretability, explainability, robustness, safet…
Multiple Instance Learning with Mixed Supervision in Gleason Grading
Hao Bian, Zhuchen Shao, Yang Chen +4
With the development of computational pathology, deep learning methods for Gleason grading through whole slide images (WSIs) have excellent prospects. Since the size of WSIs is ext…
RCMNet: A deep learning model assists CAR-T therapy for leukemia
Ruitao Zhang, Xueying Han, Ijaz Gul +8
Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although…
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Yang Liu, Ersi Zhang, Lulu Xu +13
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) s…
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
Chu Han, Xipeng Pan, Lixu Yan +40
Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can pr…