Simultaneous Detection and Classification of Partially and Weakly Supervised Cells

Alona Golts, Ido Livneh, Yaniv Zohar, Aaron Ciechanover, Michael Elad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Detection and classification of cells in immunohistochemistry (IHC) images play a vital role in modern computational pathology pipelines. Biopsy scoring and grading at the slide level is routinely performed by pathologists, but analysis at the cell level, often desired in personalized cancer treatment, is both impractical and non-comprehensive. With its remarkable success in natural images, deep learning is already the gold standard in computational pathology. Currently, some learning-based methods of biopsy analysis are performed at the tile level, thereby disregarding intra-tile cell variability; while others do focus on accurate cell segmentation, but do not address possible downstream tasks. Due to the shared low and high-level features in the tasks of cell detection and classification, these can be treated jointly using a single deep neural network, minimizing cumulative errors and improving the efficiency of both training and inference. We construct a novel dataset of Proteasome-stained Multiple Myeloma (MM) bone marrow slides, containing nine categories with unique morphological traits. With the relative difficulty of acquiring high-quality annotations in the medical-imaging domain, the proposed dataset is intentionally annotated with only 5 % of the cells in each tile. To tackle both cell detection and classification within a single network, we model these as a multi-class segmentation task, and train the network with a combination of partial cross-entropy and energy-driven losses. However, as full segmentation masks are unavailable during both training and validation, we perform evaluation on the combined detection and classification performance. Our strategy, uniting both tasks within the same network, achieves a better combined Fscore, at faster training and inference times, as compared to similar disjoint approaches.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022 Workshops, Proceedings
EditorsLeonid Karlinsky, Tomer Michaeli, Ko Nishino
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783031250651
StatePublished - 2023
Event17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duration: 23 Oct 202227 Oct 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13803 LNCS


Conference17th European Conference on Computer Vision, ECCV 2022
CityTel Aviv


  • Cell classification
  • Cell detection
  • Cell profiling

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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