Attention-guided Erasing - Bhandary Panambur, Adarsh; Yu, Hui; Bhat, Sheethal; Madhu, Prathmesh; Bayer, Siming; Maier, Andreas (Bildverarbeitung für die Medizin 2024, 2024)
The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues. This study introduces a novel data augmentation technique termed attention-guided erasing (AGE), devised to enhance the downstream classification of four distinct breast density categories in mammography following the BI-RADS recommendation in the Vietnamese cohort. The proposed method integrates supplementary information during transfer learning, utilizing visual attention maps derived from a vision transformer backbone trained using the self-supervised DINO method. These maps are utilized to erase background regions in the mammogram images, unveiling only the potential areas of dense breast tissues to the network. Through the incorporation of AGE during transfer learning with varying random probabilities, we consistently surpass classification performance compared to scenarios without AGE and the traditional random erasing transformation. We validate our methodology using the publicly available VinDr-Mammo dataset. Specifically, we attain a mean F1-score of 0.5910, outperforming values of 0.5594 and 0.5691 corresponding to scenarios without AGE and with random erasing (RE), respectively. This superiority is further substantiated by t-tests, revealing a p-value of p<0.0001, underscoring the statistical significance of our approach.<br

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

AUCReshaping: improved sensitivity at high-specificity - Bhat, Sheethal; Mansoor, Awais; Georgescu, Bogdan; Bhandary Panambur, Adarsh; Ghesu, Florin C.; Islam, Saahil; Packhäuser, Kai; Rodriguez Salas, Dalia; Grbic, Sasa; Maier, Andreas (Scientific Reports, 2023)
The evaluation of deep-learning (DL) systems typically relies on the Area under the Receiver-Operating-Curve (AU-ROC) as a performance metric. However, AU-ROC, in its holistic form, does not sufficiently consider performance within specific ranges of sensitivity and specificity, which are critical for the intended operational context of the system. Consequently, two systems with identical AU-ROC values can exhibit significantly divergent real-world performance. This issue is particularly pronounced in the context of anomaly detection tasks, a commonly employed application of DL systems across various research domains, including medical imaging, industrial automation, manufacturing, cyber security, fraud detection, and drug research, among others. The challenge arises from the heavy class imbalance in training datasets, with the abnormality class often incurring a considerably higher misclassification cost compared to the normal class. Traditional DL systems address this by adjusting the weighting of the cost function or optimizing for specific points along the ROC curve. While these approaches yield reasonable results in many cases, they do not actively seek to maximize performance for the desired operating point. In this study, we introduce a novel technique known as AUCReshaping, designed to reshape the ROC curve exclusively within the specified sensitivity and specificity range, by optimizing sensitivity at a predetermined specificity level. This reshaping is achieved through an adaptive and iterative boosting mechanism that allows the network to focus on pertinent samples during the learning process. We primarily investigated the impact of AUCReshaping in the context of abnormality detection tasks, specifically in Chest X-Ray (CXR) analysis, followed by breast mammogram and credit card fraud detection tasks. The results reveal a substantial improvement, ranging from 2 to 40%, in sensitivity at high-specificity levels for binary classification tasks.<br
Keywords: Electrical and electronic engineering; Engineering

Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning - Bhandary Panambur, Adarsh; Madhu, Prathmesh; Maier, Andreas (German Workshop on Medical Image Computing, 2022, 2022)

Enhancing downstream classification of breast abnormalities in contrast enhanced spectral mammography using a neighborhood representation loss - Bhandary Panambur, Adarsh; Madhu, Prathmesh; Bayer, Siming; Maier, Andreas (Medical Imaging 2024: Computer-Aided Diagnosis, 2024)
Contrast-enhanced spectral mammography (CESM) has proven to be a beneficial tool for additional management and diagnosis of breast cancer in patients. Deep learning-based automated tools for analyzing CESM images and detecting abnormalities can be crucial in reducing the workload of radiologists. Even though self-supervised learning (SSL) can significantly influence the quality of learned representations resulting in increased classification performances, it requires large amounts of data often unavailable in the CESM domain. In this work, we propose a novel loss function named Difficulty-Weighted Neighborhood Representation (DWNR) utilizing the SSL-based representations designed explicitly for the downstream classification of the region of interest (ROI) in CESM images in a small dataset setting. We propose utilizing the neighborhood representations extracted from an SSL backbone to guide the loss function in learning robust representations of the dataset’s normal, benign, and malignant ROIs. We formulate the DWNR loss using a combination of a neighborhood representation-guided triplet (NR-triplet) loss and a difficulty-weighted cross-entropy (DWCE) loss. The NR-triplet loss leverages the nearest and farthest neighbor feature representations, extracted from a self-supervised backbone, as positive and negative samples. Meanwhile, the DWCE loss exploits the class information from the nearest neighbors to perform per-sample difficulty weighting. The experimental results on the publicly available CDD-CESM dataset demonstrate that the proposed DWNR loss outperforms traditional loss functions such as Cross-Entropy (CE) and Focal loss by a significant gain of more than 2% absolute mean F1-scores for both SSL-based and ImageNet-SSL-based transfer learning. In addition, we demonstrate the efficacy of employing a DWNR-based ROI classifier for comprehensive CESM whole image analysis in BI-RADS scoring and cancer diagnosis. The results reveal a gain in mean F1-scores of more than 1% in both tasks when using the DWNR loss models for CNN initialization, indicating the significance of the proposed method to whole image analysis. Based on our analysis, incorporating DWNR loss during transfer learning could positively impact automated breast cancer diagnosis using the CESM imaging modality. This novel loss function opens new possibilities for leveraging deep learning in CESM, particularly in scenarios with limited labeled data availability.<br
Keywords: Machine learning; Feature extraction; Image classification; Deep learning; Breast; Breast cancer; Image analysis

Enhancing mammography screening sensitivity with AI-assistance: Evidence from a Vietnamese study cohort - Bhandary Panambur, Adarsh (2024)
Purpose In the Vietnamese population, a notably higher incidence of dense breast tissue in women presents significant challenges for mammographic screening, as the dense tissue can obscure underlying lesions, thereby reducing the sensitivity of detection [1]. The American College of Radiology has developed the Breast Imaging-Reporting and Data System (BI-RADS), which provides a uniform system for categorizing findings from breast imaging studies, including mammography, ultrasound, and MRI [2]. This research assesses the effectiveness of BI-RADS assessments when radiologists employ the Transpara Breast AI in mammographic screenings,... Methods and materials A prospective study was carried out at Hanoi Medical University Hospital from November 2022 to April 2023, including female patients who were scheduled for mammography following ultrasound or manual breast examinations. The selection process was rigorous, employing exclusion criteria to maintain data integrity: Definitive prior diagnostics. Small breast size. Presence of breast implants. A BI-RADS assessment of below 1. After applying these criteria, the study included a final cohort of 1,119 eligible patients. To ensure the study's robustness: Initial mammograms were analyzed by an experienced... Results The evaluation of screening sensitivities within mammographic assessments, comparing the AI-supported workflow of Reader 2 against the traditional approach of Reader 1, was meticulously conducted through Receiver Operating Characteristic (ROC) curve assessments, utilizing the BI-RADS scoring system as a standardized measure. Figure 1 illustrates the Receiver Operating Characteristic (ROC) curve comparison analysis, which includes the ROC curves for individual BI-RADS scores.[Fig 1]The results of this comparative study are summarized as follows: Utilizing Reader 1's evaluations as the baseline, the integration of AI with Reader 2's... Conclusion In conclusion, we observe that incorporating AI assistance into mammography screening has demonstrated increased sensitivity, a development particularly advantageous for the Vietnamese and broader Southeast Asian populations, characterized by a higher prevalence of dense breast tissue. This study underscores the significant potential of AI-supported mammographic analysis to enhance the accuracy and reliability of breast cancer detection within these groups. This encourages the broader use of AI in breast cancer screening, particularly where dense breast tissue makes diagnosis more difficult.
Keywords: Artificial Intelligence; Breast; Mammography; Comparative studies; Observer performance; Cancer

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