Simultaneous Detection and Classification of Partially and Weakly Supervised Cells

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

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

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.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفComputer Vision – ECCV 2022 Workshops, Proceedings
المحررونLeonid Karlinsky, Tomer Michaeli, Ko Nishino
ناشرSpringer Science and Business Media Deutschland GmbH
الصفحات313-329
عدد الصفحات17
رقم المعيار الدولي للكتب (المطبوع)9783031250651
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2023
الحدث17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, إسرائيل
المدة: ٢٣ أكتوبر ٢٠٢٢٢٧ أكتوبر ٢٠٢٢

سلسلة المنشورات

الاسمLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
مستوى الصوت13803 LNCS

!!Conference

!!Conference17th European Conference on Computer Vision, ECCV 2022
الدولة/الإقليمإسرائيل
المدينةTel Aviv
المدة٢٣/١٠/٢٢٢٧/١٠/٢٢

All Science Journal Classification (ASJC) codes

  • !!Theoretical Computer Science
  • !!General Computer Science

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