Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks
Cancer prognostication is a challenging task in computational pathology that requires
context-aware representations of histology features to adequately infer patient survival …
context-aware representations of histology features to adequately infer patient survival …
Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer
Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment and treatment
planning. However, there are few known biomarkers that are robust enough to show true …
planning. However, there are few known biomarkers that are robust enough to show true …
Graph moving object segmentation
JH Giraldo, S Javed… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to
undesirable variations in the background scene, MOS becomes very challenging for static …
undesirable variations in the background scene, MOS becomes very challenging for static …
ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis
Whole slide image (WSI) analysis has become increasingly important in the medical
imaging community, enabling automated and objective diagnosis, prognosis, and …
imaging community, enabling automated and objective diagnosis, prognosis, and …
Two ensemble-CNN approaches for colorectal cancer tissue type classification
E Paladini, E Vantaggiato, F Bougourzi, C Distante… - Journal of …, 2021 - mdpi.com
In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital
Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the …
Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the …
Nucleus classification in histology images using message passing network
Identification of nuclear components in the histology landscape is an important step towards
developing computational pathology tools for the profiling of tumor micro-environment. Most …
developing computational pathology tools for the profiling of tumor micro-environment. Most …
Fhist: A benchmark for few-shot classification of histological images
Few-shot learning has recently attracted wide interest in image classification, but almost all
the current public benchmarks are focused on natural images. The few-shot paradigm is …
the current public benchmarks are focused on natural images. The few-shot paradigm is …
Knowledge distillation in histology landscape by multi-layer features supervision
Automatic tissue classification is a fundamental task in computational pathology for profiling
tumor micro-environments. Deep learning has advanced tissue classification performance at …
tumor micro-environments. Deep learning has advanced tissue classification performance at …
A graph neural network framework for mapping histological topology in oral mucosal tissue
A Nair, H Arvidsson, JE Gatica V, N Tudzarovski… - BMC …, 2022 - Springer
Background Histological feature representation is advantageous for computer aided
diagnosis (CAD) and disease classification when using predictive techniques based on …
diagnosis (CAD) and disease classification when using predictive techniques based on …
Spatially constrained context-aware hierarchical deep correlation filters for nucleus detection in histology images
Nucleus detection in histology images is a fundamental step for cellular-level analysis in
computational pathology. In clinical practice, quantitative nuclear morphology can be used …
computational pathology. In clinical practice, quantitative nuclear morphology can be used …