A review of machine learning approaches, challenges and prospects for computational tumor pathology

31 May 2022

Papers citing "A review of machine learning approaches, challenges and prospects for computational tumor pathology"

5 / 5 papers shown

Title
RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Shengchun Xiong Xiangru Li Yunpeng Zhong Wanfen Peng 62 0 0 08 May 2025
A Lightweight and Extensible Cell Segmentation and Classification Model for Whole Slide Images N. Shvetsov T. Kilvaer M. Tafavvoghi Anders Sildnes Kajsa Møllersen Lill-ToveRasmussen Busund L. A. Bongo VLM 66 1 0 26 Feb 2025
Rotation-Agnostic Image Representation Learning for Digital Pathology Saghir Alfasly A. Shafique Peyman Nejat Jibran A. Khan Areej Alsaafin Ghazal Alabtah H. R. Tizhoosh MedIm 24 6 0 14 Nov 2023
InceptionTime: Finding AlexNet for Time Series Classification Hassan Ismail Fawaz Benjamin Lucas Germain Forestier Charlotte Pelletier Daniel F. Schmidt J. Weber Geoffrey I. Webb L. Idoumghar Pierre-Alain Muller Franccois Petitjean AI4TS 85 1,071 0 11 Sep 2019
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition Zifeng Wu Chunhua Shen A. Hengel SSeg 245 1,490 0 30 Nov 2016