Attention-guided erasing for enhanced transfer learning in breast abnormality classification - Bhandary Panambur, Adarsh; Bhat, Sheethal; Yu, Hui; Madhu, Prathmesh; Bayer, Siming; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2025)
Purpose Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography. Methods AGE utilizes attention head visualizations from DINO self-supervised pretraining to weakly localize regions of interest (ROI) in images. These localizations are then used to stochastically erase non-essential background information from training images during transfer learning. Our research evaluates AGE across two image-level and three patch-level classification tasks. The image-level tasks involve breast density categorization in digital mammography (DM) and malignancy classification in contrast-enhanced mammography (CEM). Patch-level tasks include classifying calcifications and masses in scanned film mammography (SFM), as well as malignancy classification of ROIs in CEM. Results AGE significantly boosts classification performance with statistically significant improvements in mean F1-scores across four tasks compared to baselines. Specifically, for image-level classification of breast density in DM and malignancy in CEM, we achieve gains of 2% and 1.5%, respectively. Additionally, for patch-level classification of calcifications in SFM and CEM ROIs, gains of 0.4% and 0.6% are observed, respectively. However, marginal improvement is noted in the mass classification task, indicating the necessity for further optimization in tasks where critical features may be obscured by erasing techniques. Conclusion Our findings underscore the potential of AGE, a dataset- and task-specific augmentation strategy powered by self-supervised learning, to enhance the downstream classification performance of DL models, particularly involving ViTs, in medical imagin

Molecular structure of octadecylphosphonic acids during their self-assembly on α-Al2O3(0001) - Meltzer, Christian; Yu, Hui; Peukert, Wolfgang; Braunschweig, Björn (Physical Chemistry Chemical Physics, 2018)

UNSTAINED WHITE BLOOD CELL CLASSIFICATION USING DEEP LEARNING - Yu, Hui; Forster, Frank; Bhandary Panambur, Adarsh; Merino, Anna; Maier, Andreas; Marquardt, Gaby (Abstracts of International Society for Laboratory Hematology XXXVIth International Symposium on Technical Innovations in Laboratory Hematology, 2023)
Introduction: Hematological diseases, including leukemia, lymphoma, etc., are associated with White Blood Cell (WBC) disorders. Therefore, precise classification of WBCs is critical in peripheral blood (PB) smear analysis. However, the traditional staining process on the smears is time-consuming and expensive. Additionally, the chemical residuals are detrimental to the operators and the environment. In this work, we develop a two-stage deep learning (DL) pipeline for the classification of 14 different types of unstained WBC. Methods: PB smears of 233 different patients were collected from two different hospitals. These PB smears were microscopically digitalized before and after staining and a dataset consisting of both normal and abnormal WBC digital images was acquired. The stained cell images were initially annotated by two clinical experts. These annotations were then transferred to the unstained ones by alignment. A 14 different classes of blood cells have been annotated. The 14 types of WBC images are as follows: Basophil granulocyte (BA), blast (BL), eosinophil granulocyte (EO), immature granulocyte (IG), neutrophil granulocyte (NE), lymphocyte (LY), abnormal lymphoid cell (ALC), monocyte (MO), reactive lymphocyte (RL), plasma cell (PC), nucleated red blood cell (NRBC), giant platelet (GPLT), smudge (SMU) and artifact (ART). The total number of unstained images is 339 885. A 70%, 17%, and 13% of the total dataset were used for training, validation, and testing, respectively. As shown in Fig. 1, a patient-wise classification strategy with a transformer was designed to improve the classification rate using a pre-trained ResNet-18 as the feature extractor. This was performed to potentially mimic how pathologists classify by comparing different cells of the same patient to classify the normal and pathological cells. Results: Performing the evaluation on the testing set for both ResNet-18 and transformer models, mean F1 weighted scores of 90.33% and 91.77% are achieved, respectively. Thus, transformer approach with patient-wise method outperforms ResNet-18 by 1.44%. Fig. 2 shows the density confusion matrix of the best model with patient-wise transformer strategy. Even though the classification of normal WBCs is robust, the identification of some abnormal types still requires improvement. Conclusion: The proposed two-stage DL method shows promising results on unstained WBC classification. The transformer approach improves the classification performance on top of conventional ResNet-18 using patient-wise strategy.<br
Keywords: Deep Learning; White Blood Cells; Unstained