Brent Mombourquette's research while affiliated with Santa Clara University and other places
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Publications (12)
This work reveals undiscovered challenges in the performance and generalizability of deep learning models. We (1) identify spurious shortcuts and evaluation issues that can inflate performance and (2) propose training and analysis methods to address them. We trained an AI model to classify cancer on a retrospective dataset of 120,112 US exams (3,46...
![Fig. 4: Two mammography classification architectures: GMIC [30] and our...](profile/Ahmed-Taha/publication/363592040/figure/fig4/AS:11431281232634116@1711743720043/Two-mammography-classification-architectures-GMIC-30-and-our-HCT-Both-architectures_Q320.jpg)
Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling long range dependencies. Shallow transformers eliminate this problem, but they suffer from quadratic complexity. In this paper, we tackle this complexity by leveraging a...
![Fig. 4: Two mammography classification architectures: GMIC [30] and our...](profile/Ahmed-Taha/publication/362708473/figure/fig4/AS:11431281119031041@1675974221176/Two-mammography-classification-architectures-GMIC-30-and-our-HCT-Both-architectures_Q320.jpg)
Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling long range dependencies. Shallow transformers eliminate this problem, but they suffer from quadratic complexity. In this paper, we tackle this complexity by leveraging a...
![Fig. 4: Two mammography classification architectures: GMIC [30] and our...](profile/Ahmed-Taha/publication/362694134/figure/fig4/AS:11431281079104263@1660532877424/Two-mammography-classification-architectures-GMIC-30-and-our-HCT-Both-architectures_Q320.jpg)
Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling long range dependencies. Shallow transformers eliminate this problem, but they suffer from quadratic complexity. In this paper, we tackle this complexity by leveraging a...
Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause unnecessary procedures, patient anxiety, and financial burden. This work demonstrates an AI algorithm that reduces false positives by identifying mammograms not suspicio...
Purpose:
To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multisite setting for synthetic two-dimensional mammographic (SM) images derived from digital breast tomosynthesis examinations by using full-field digital mammographic (FFDM) images and limited SM data.
Materials and methods:
A...
Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exams or procedures, and low sensitivity. Digital breast tomosynthesis (DBT) improves on conventional mammography by increasing both sensitivity and specificity and is becom...
\textbf{Purpose:}$ To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density DL model in a multi-site setting for synthetic 2D mammography (SM) images derived from 3D DBT exams using FFDM images and limited SM data. $\textbf{Materials and Methods:}$ A DL model was trained to predict BI-RADS breast density using FFDM images acqu...
Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population of women seeking screening mammography. Although commercial computer aided detection (CADe) software has been available to radiologists for decades, it has failed to imp...
Citations
... 3a reports M&M's breast-level and exam-level classification results on OPTIMAM and the two inhouse datasets. We use GMIC [23] and HCT [25] as baselines since they are open-sourced classifiers developed for mammography. All three models were trained only on OPTIMAM. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/11431281237762308-1713627150244_Q64/Ahmed-Taha.jpg)
... We report free response operating characteristic (FROC) curves and recalls at various FP/image (R@t). Following [3,5,16,29], a proposal is considered true positive if its center lies within the ground truth box. For classification, we report the area under the receiver operating characteristic curve (AUC). ...
... More recently, deep learning-based methods have been proposed for full four-class BI-RADS classification [14][15][16][17][18] , and binary dense/non-dense classification using full-field digital mammography (FFDM) 19,20 , with promising performance and good agreement with expert radiologists. In clinical practice, both FFDM and two-dimensional synthetic (2DS) images generated from digital breast tomosynthesis (DBT) are used in screening and diagnostic imaging. ...
... The researchers compared these methods using both histogram-matched MIP images and original MIP images. The findings revealed that the best performance, in terms of (AUC = 0.847) was achieved by fine-tuning the last two layers with MIP-HM [25]. ...
