[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83042-en":3,"doc-seo-83042-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83042,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","MSA-DCNN: A Data-Efficient Multi-Scale Deformable CNN for Medical Image Classification","Deep learning models for medical image classification can be limited by fixed sampling, multi-scale morphological complexity, and scarce annotations, which weakens performance when anatomy varies structurally across resolutions. MSA-DCNN introduces a scale-consistent deformable attention framework with adaptive multi-scale sampling, within-scale saliency refinement, learned cross-scale fusion, and auxiliary self-distillation under a unified optimisation scheme. Experiments on three public benchmarks plus an external leukemia hold-out show improved accuracy, F1, and binary AUC under distribution shift and label scarcity while using fewer parameters, supported by ablations.","arXiv :2607 .06083v 1 [ cs .CV] 7 Jul 2026  \nMSA-DCNN: A Data-Efficient Multi-Scale Deformable CNN for Medical Image Classification  \nHamza Hussaini 1[0009−0005−6463−1607], Shahana Bano 1[0000−0002−6907−1777], Eyad Elyan 1[0000−0002−8342−9026], and Carlos Francisco Moreno-García 1[0000−0001−7218−9023]  \nRobert Gordon University, School of Computing, Engineering and Technology,  \nAberdeen, Scotland, UK  \nh.hussaini;s.bano;e.elyan;[c.moreno-garcia@rgu.ac.uk](c.moreno-garcia@rgu.ac.uk)[ ](c.moreno-garcia@rgu.ac.uk)[http://www.rgu.ac.uk](http://www.rgu.ac.uk)  \nAbstract. Existing deep learning methods perform well in medical image classification but struggle with multi-scale morphology and limited annotations due to fixed sampling and data-hungry training. Existing approaches address these challenges in isolation: DCN-based models provide adaptive sampling but lack explicit multi-scale attention fusion and label-efficient regularisation; multi-scale architectures typically rely on static fusion; and semi-supervised methods target label scarcity without jointly modelling adaptive cross-scale representations. We propose MSADCNN, a scale-consistent deformable attention learning framework that introduces adaptive multi-scale sampling, within-scale saliency refinement, learned cross-scale fusion, and auxiliary self-distillation within a unified optimisation scheme, with potential to generalise to structurally heterogeneous anatomy. We evaluate on three public benchmarks and an external hold-out set for leukaemia. MSA-DCNN demonstrates competitive and often better performance against ViT baselines, CNN baselines, and a MICCAI semi-supervised baseline under distribution shift and label scarcity in accuracy, F1, and AUC (binary), while using fewer parameters. Ablations confirm complementary component contributions, supporting MSA-DCNN as a practical foundation for data-efficient medical image classification.  \nKeywords: Multi-scale attention learning · Deformable convolutional networks · Data-efficient medical image classification · Scale-consistent feature fusion · Self-distillation learning.  \n1 Introduction  \nAutomated medical image classification is a key enabler of computational healthcare, delivering fast, objective, and reproducible analysis across radiography, histopathology, and microscopy [12,18] . Convolutional neural networks (CNNs) remain the workhorse due to their hierarchical feature learning and strong performance across lung, brain, oncology, and haematology tasks [29,6,20,16,17] . Yet,  \n2 Hussaini et al.  \nstandard CNNs inherit fixed receptive fields and uniform sampling, which are often poorly matched to the heterogeneous textures and multi-scale structural variability of medical imagery [28,24,26] . Prior remedies, including attention modulesand multi-scale designs, improve feature focus and coverage [21,8,10,30], but often rely on global aggregation that can wash out subtle cues [9], retain fixed kernels that do not adapt sampling across scales [27], and rarely provide internal supervision to strengthen shallow layers in data-limited regimes [13] . Moreover, medical image classification is frequently constrained by limited labelled data [11,14,15,3], limiting generalisation and sensitivity to minority patterns.  \nWe address these gaps with the Multi-Scale Attention Deformable Convolutional Neural Network (MSA-DCNN) . This work introduces a general principle for scale-consistent learning under structural heterogeneity and label scarcity, designed to perform effectively at low label fractions with a lean parameter budget while enforcing semantic alignment across resolution levels. Critically, attention is applied scale-specifically, and the resulting features are fused by a learned multi-scale attention mechanism, coupling within-scale recalibration with across-scale selection, an integration not explicitly studied in prior deformable or attention-based CNNs.  \nWe evaluate MSA-DCNN on three publicly available benchmark","cbCaitjJytOzNm63","https://ap.wps.com/l/cbCaitjJytOzNm63","pdf",665354,2,1,11,"English","en",105,"# Introduction\n# Methodology","[{\"question\":\"What problem does MSA-DCNN address in medical image classification?\",\"answer\":\"It targets poor matching between fixed receptive fields and heterogeneous, multi-scale structures, alongside limited labeled data that harms generalisation and sensitivity to minority patterns.\"},{\"question\":\"How does MSA-DCNN perform multi-scale feature learning?\",\"answer\":\"It uses adaptive multi-scale deformable sampling, then refines saliency within each scale and performs learned cross-scale fusion through multi-scale attention over projected embeddings.\"},{\"question\":\"How was MSA-DCNN evaluated and what were the main results?\",\"answer\":\"MSA-DCNN was evaluated on three public benchmarks and an external leukemia hold-out set, achieving competitive or better performance than ViT, CNN, and a MICCAI semi-supervised baseline under distribution shift and label scarcity on accuracy, F1, and binary AUC while using fewer 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problem does MSA-DCNN address in medical image classification?","Question",{"text":75,"@type":76},"It targets poor matching between fixed receptive fields and heterogeneous, multi-scale structures, alongside limited labeled data that harms generalisation and sensitivity to minority patterns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does MSA-DCNN perform multi-scale feature learning?",{"text":80,"@type":76},"It uses adaptive multi-scale deformable sampling, then refines saliency within each scale and performs learned cross-scale fusion through multi-scale attention over projected embeddings.",{"name":82,"@type":73,"acceptedAnswer":83},"How was MSA-DCNN evaluated and what were the main results?",{"text":84,"@type":76},"MSA-DCNN was evaluated on three public benchmarks and an external leukemia hold-out set, achieving competitive or better performance than ViT, CNN, and a MICCAI semi-supervised baseline under distribution shift and label scarcity on accuracy, F1, and 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