Automated breast cancer classification from whole-slide images (WSIs) is hampered by the scarcity of expert-annotated patches and the domain variability inherent to multi-institutional datasets. We propose and compare two semi-supervised frameworks for patch-level tumor classification under limited annotation. The firs...
T. Zhaksylyk, Aruzhan Imasheva, B. Abdikenov et al.· Frontiers in Artificial Inte...· 0 citations
Labeled clinical text is scarce in many non-English healthcare settings, limiting the development of robust clinical NLP systems. We tested whether supervision from Spanish mammography reports improved the classification of Russian-language reports from Kazakhstan. The study included 4279 Spanish and 495 Russian report...
Anuar Dosmaganbetov, T. Zhaksylyk, B. Abdikenov· Information· 0 citations
Background: Public breast ultrasound datasets are assembled around lesions. Of eight sources examined here, five contain no normal images, and under leave-one-dataset-out evaluation, the normal class is therefore almost perfectly confounded with the acquisition source: with one site held out, 356 of the 358 normal trai...
B. Abdikenov, Aruzhan Imasheva, Dauren Izdibay et al.· Information· 0 citations
A systematic literature review following the PRISMA guidelines to examine artificial intelligence methods for handwritten text recognition (HTR) and text restoration in low-resource languages and proposes a concrete development roadmap focusing on systematic digitization, expert annotation, transfer learning, and the c...
Zhanibek Balabayev, S. Biloshchytska, Beibit Abdikenov et al.· Information· 0 citations
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