Skip to main content Skip to navigation

TIA Centre: Software

  • Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations (accepted for publication in IEEE TMI, Jan 2021)
  • NuClick: Interactive segmentation of nuclei in histology images (Medical Image Analysis, Oct 2020)
  • HoVer-Net: SOTA algorithm for simultaneous segmentation and classification of nuclei in H&E images (Medical Image Analysis, Dec 2019)
  • MILIAMP: Multiple Instance Learning of Amyloid Proteins (Minhas)
  • AMAP: A machine learning predictor for antimicrobial activity (Minhas)
  • LUPI-SVM: LUPI-SVM based classification of protein complexes using binding affinity (Minhas)
  • Hepatic-Index: Predicts Liver Cirrhosis from ultrasound images (Minhas)
  • HoPItor: Host-Pathogen Protein Interaction Prediction (Minhas)
  • CaMels: Calmodulin (CaM) intEraction Learning System (Minhas)
  • CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data (Minhas)
  • SC-CNN: Deep learning code for cell detection and phenotyping in H&E images (Feb 2017) [ code for our TMI May 2016 paper; available on request by email to Nasir Rajpoot; Labelled cell nuclei data ]
  • Cell Words for modelling the appearance of cells in histopathology images (March 2015)
  • Stain normalization toolbox for histopathology images (Feb 2015)
  • THeCoT: Spatial Model of Tumour Heterogeneity in Colorectal Adenocarcinoma (BMC Bioinformatics, 2015)
  • DiSWOP: Cell localised protein network analysis (Bioinformatics, 2014)
  • GBHC: Bayesian Hierarchical Clustering with a Gaussian Conjugate Prior for clustering of Gene Expression profiling data (PLoS ONE, 2014)
  • Cell/Nuclei Detection using LIPSyM for H&E and H&DAB histopathology images (J Path Informatics, 2012)