AbstractDigital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT’s diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 $$\times$$ × 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model’s decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.

IntroductionEnvironmental and genetic factors contribute to the increased risk for neurodevelopmental disorders, including deficits in the development of social communication. In the mouse, ultrasonic vocalizations emitted by the pup stimulate maternal retrieval and potentiate maternal care. Therefore, isolation induced ultrasonic vocalization emitted by pups provides a means to evaluate deficits in communication during early development, before other ways of communication are apparent. Previous studies in our labs showed that gestational exposure to the pesticide chlorpyrifos (CPF) and the Methylenetetrahydrofolate (Mthfr)-knock-out mice are associated with impaired social preference, restricted or repetitive behavior and altered spectral properties of pups’ ultrasonic vocalization. In this study, we explore the temporal dynamics of pups’ vocalization in these Autism spectrum disorder (ASD) models.MethodsWe utilized the maternal potentiation protocol and analyzed the time course of pup vocalizations following isolation from the nest. Two models of ASD were studied: gestational exposure to the pesticide CPF and the Mthfr-knock-out mice.ResultsVocalization emitted by pups of both ASD models were dynamically modified in quantity and spectral structure within each session and between the two isolation sessions. The first isolation session was characterized by a buildup of call quantity and significant effects of USV spectral structure variables, and the second isolation session was characterized by enhanced calls and vocalization time, but minute effect on USV properties. Moreover, in both models we described an increased usage of harmonic calls with time during the isolation sessions.DiscussionCommunication between two or more individuals requires an interplay between the two sides and depends on the response and the time since the stimulus. As such, the presence of dynamic changes in vocalization structure in the control pups, and the alteration observed in the pups of the ASD models, suggest impaired regulation of vocalization associated with the environmental and genetic factors. Last, we propose that temporal dynamics of ultrasonic vocalization communication should be considered in future analysis in rodent models of ASD to maximize the sensitivity of the study of vocalizations.

Studies in rodent models suggest that calls emitted by isolated pups serve as an early behavioral manifestation of communication deficits and autistic like behavior. Previous studies in our labs showed that gestational exposure to the pesticide chlorpyrifos (CPF) and the Mthfr-knock-out mice are associated with impaired social preference and restricted or repetitive behavior. To extend these studies, we examine how pup communication via ultrasonic vocalizations is altered in these ASD models. We implemented an unsupervised hierarchical clustering method based on the spectral properties of the syllables in order to exploit syllable classification to homogeneous categories while avoiding over-categorization. Comparative exploration of the spectral and temporal aspects of syllables emitted by pups in two ASD models point to the following: (1) Most clusters showed a significant effect of the ASD factor on the start and end frequencies and bandwidth and (2) The highest percent change due to the ASD factor was on the bandwidth and duration. In addition, we found sex differences in the spectral and temporal properties of the calls in both control groups as well as an interaction between sex and the gene/environment factor. Considering the basal differences in the characteristics of syllables emitted by pups of the C57Bl/6 and Balb/c strains used as a background in the two models, we suggest that the above spectral-temporal parameters start frequency, bandwidth, and duration are the most sensitive USV features that may represent developmental changes in ASD models.

Complications related to the misplacement and dislodging of endotracheal intubation can be lethal. A novel artificial intelligence system for endotracheal intubation confirmation is described, based on the processing of images acquired during the intubation and identification of carina images. The system is comprised of a miniature metal oxide silicon sensor (CMOS) attached to the tip of a semi-rigid stylet connected to a digital signal processor (DSP) with an integrated video acquisition component. Video images are acquired and processed by a deep learning algorithm implemented on the DSP. The algorithm calculates the probability that the image belongs to the carina model that was a-priori trained and saved, and used for the classification decision. System performance was assessed on intubation videos recorded from 10 subjects who underwent general anesthesia during elective operations. The videos were annotated by an anesthesiologist, such that each video image was classified according to the anatomical position. A leave-one-case-out method was employed, such that in each iteration, images from one subject were used to train the system and estimate the carina model, and the remaining images were used to assess system performance. This process was repeated 10 times such that each subject participated once in testing. The results showed that this fully automatic image recognition system confirmed the correct tube positioning in all cases, for a 100% success rate.

This paper addresses the problem of mass detection in mammograms. It has long ago been shown that computer-aided diagnosis (CAD) schemes have the potential of improving breast cancer diagnosis performance. We propose a CAD scheme based on convolutional neural networks, using transfer representation learning and the Google Inception-V3 architecture. Artificially generated mammograms and data augmentation techniques are used to expand and balance the available database at train time. The performance of the proposed scheme is evaluated based on the receiver operating characteristics (ROC) curve. Areas under the ROC curve of 0.78 and 0.86 were obtained using artificially-generated mammograms and augmentation, respectively.

PURPOSE:This study aims to develop and test a new computer-aided detection (CAD) approach and scheme, assessing the likelihood of a subject harboring breast abnormalities.METHODS:The proposed scheme is based on the analysis of both local and global bilateral mammographic feature asymmetries. The level of local or global asymmetry is assessed by analyzing mammographic features extracted from the bilaterally matched regions of interest (ROIs), or from the entire breast, respectively. The selected local and global feature vectors are combined and classified using a maximum likelihood obtained from a naïve Bayes classifier. This scheme was evaluated using a leave-one-case-out cross-validation method that was applied to 243 subjects from mini-MIAS and INbreast databases. In addition, the result is compared with a conventional unilateral (or single) image-based CAD scheme.RESULTS:Using a case-based evaluation approach and an area under curve (AUC) of the receiver operating characteristic (ROC) as a performance index, the new scheme yielded AUC = 0.79 ± 0.07, an 8.2% increase compared with AUC = 0.73 ± 0.08 obtained using the unilateral image-based CAD scheme.CONCLUSIONS:This work demonstrates that applying bilateral asymmetry analysis increases the discriminatory power of CAD schemes while optimizing the likelihood assessment of breast abnormalities presence. Therefore, the proposed CAD approach provides the radiologist with beneficial supplementary information and can indicate high-risk cases.

Breast cancer is the most prevalent cancer among women. The most common method to detect breast cancer is mammography. However, interpreting mammography is a challenging task that requires high skills and is timeconsuming. In this work, we propose a computer-aided diagnosis (CAD) scheme for mammography based on transfer representation learning using the Inception-V3 architecture. We evaluate the performance of the proposed scheme using the INBreast database, where the features are extracted from different layers of the architecture. In order to cope with the small dataset size limitation, we expand the training dataset by generating artificial mammograms and employing different augmentation techniques. The proposed scheme shows great potential with a maximal area under the receiver operating characteristics curve of 0.91.

In this paper, a Computer-Aided Diagnosis (CAD) system for endotracheal tube position confirmation, and detection of errors in intubation positioning is presented. Endotracheal intubation is a complex procedure which requires high skills and the use of secondary confirmation devices to ensure correct positioning of the tube. Our novel confirmation approach is based on video images classification and specifically on identification of specific anatomical landmarks, including esophagus, upper trachea and main bifurcation of the trachea into the two primary bronchi (“carina”), as indicators of correct or incorrect tube insertion and positioning. Classification of the images is performed using a neural network classifier. The performance of the proposed approach was evaluated using a dataset of cow-intubation videos and a dataset of humanintubation videos. Each one of the video images was manually (visually) classified by a medical expert into one of three categories: upper tracheal intubation, correct (carina) intubation and esophageal intubation. The image classification algorithm was applied off-line using a leave-one-case-out method. The results show that the system correctly classified 1567 out of 1600 (97.9%) of the cow intubations images, and 349 out of the 358 human intubations images (97.5%).

This paper addresses the problem of classification of upper airways images for endotracheal intubation verification in order to improve the safety of patients undergoing general anaesthesia. The proposed method is based on textural features utilized in a continuous probabilistic framework using parallel Gaussian mixture models (GMMs). The classification decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. Evaluation of the proposed approach is done using a dataset of 200 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.

Endotracheal intubation is a complex medical procedure in which a ventilating tube is inserted into the human trachea. Improper positioning carries potentially fatal consequences and therefore confirmation of correct positioning is mandatory. In this paper we report the results of using a neural network-based image classification system for endotracheal tube position confirmation. The proposed system comprises a miniature complementary metal oxide silicon sensor (CMOS) attached to the tip of a semi rigid stylet and connected to a digital signal processor (DSP) with an integrated video acquisition component. Video signals are acquired and processed by a confirmation algorithm implemented on the processor. The performance of the proposed algorithm was evaluated using two datasets: a dataset of 250 images of the upper airways. The results, obtained using a leave-one-case-out method, show that the system correctly classified 240 out of 250 (96.0%).

Accurate segmentation of breast lesions depicting on two-dimensional projection mammograms has been proven very difficult and unreliable. In this study we investigated a new approach of a computer-aided detection (CAD) scheme of mammograms without lesion segmentation. Our scheme was developed based on the detection and analysis of region-of-interest (ROI)-based bilateral mammographic tissue or feature asymmetry. A bilateral image registration, image feature selection process, and naïve Bayes linear classifier were implemented in CAD scheme. CAD performance predicting the likelihood of either an ROI or a subject (case) being abnormal was evaluated using 161 subjects from the mini-MIAS database and a leave-one-out testing method. The results showed that areas under receiver operating characteristic (ROC) curves were 0.87 and 0.72 on the ROI-based and case-based evaluation, respectively. The study demonstrated that using ROI-based bilateral mammographic tissue asymmetry can provide supplementary information with high discriminatory power in order to improve CAD performance.

This paper addresses the problem of medical image representation and classification. A texture-based continuous probabilistic framework is presented, according to which images taken at different angles are represented using several probabilistic models connected in parallel. Classification of the images is performed using a parallel Gaussian mixture models (GMMs) framework, which is composed of several GMMs, schematically connected in parallel, where each GMM represents a different imaging angle. The classification decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. Evaluation of the proposed approach is done using a dataset of 100 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.

Current computer-aided detection (CAD) schemes for detecting mammographic masses have several limitations including high correlation with radiologists' detection and cueing most subtle masses only on one view. To increase CAD sensitivity in cueing more subtle masses that are likely missed and/or overlooked by radiologists without increasing false-positive rates, we investigated a new case-dependent cueing method by combining the original CAD-generated detection scores with a computed bilateral mammographic density asymmetry index. Using the new method, we adaptively raise the CAD-generated scores of the regions detected on 'high-risk' cases to cue more subtle mass regions and reduce the CAD scores of the regions detected on 'low-risk' cases to discard more false-positive regions. A testing dataset involving 78 positive and 338 negative cases was used to test this adaptive cueing method. Each positive case involves two sequential examinations in which the mass was detected in 'current' examination and missed in 'prior' examination but detected in a retrospective review by radiologists. Applying to this dataset, a pre-optimized CAD scheme yielded 75% case-based and 55% region-based sensitivity on 'current' examinations at a false-positive rate of 0.25 per image. CAD sensitivity was reduced to 42% (case based) and 27% (region based) on 'prior' examinations. Using the new cueing method, case-based and region-based sensitivity could maximally increase 9% and 33% on the 'prior' examinations, respectively. The percentages of the masses cued on two views also increased from 27% to 65%. The study demonstrated that using this adaptive cueing method enabled us to help CAD cue more subtle cancers without increasing the false-positive cueing rate.

Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists “a visual aid” in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting “abnormalities” similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 (p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.

Bilateral mammographic tissue density asymmetry could be an important factor in assessing risk of developing breast cancer and improving the detection of the suspicious lesions. This study aims to assess whether fusion of the bilateral mammographic density asymmetrical information into a computer-aided detection (CAD) scheme could improve CAD performance in detecting mass-like breast cancers. A testing dataset involving 1352 full-field digital mammograms (FFDM) acquired from 338 cases was used. In this dataset, half (169) cases are positive containing malignant masses and half are negative. Two computerized schemes were first independently applied to process FFDM images of each case. The first single-image based CAD scheme detected suspicious mass regions on each image. The second scheme detected and computed the bilateral mammographic tissue density asymmetry for each case. A fusion method was then applied to combine the output scores of the two schemes. The CAD performance levels using the original CAD-generated detection scores and the new fusion scores were evaluated and compared using a free-response receiver operating characteristic (FROC) type data analysis method. By fusion with the bilateral mammographic density asymmetrical scores, the case-based CAD sensitivity was increased from 79.2% to 84.6% at a false-positive rate of 0.3 per image. CAD also cued more "difficult" masses with lower CAD-generated detection scores while discarded some "easy" cases. The study indicated that fusion between the scores generated by a single-image based CAD scheme and the computed bilateral mammographic density asymmetry scores enabled to increase mass detection sensitivity in particular to detect more subtle masses.

In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represents a particular channel. The performance of the proposed algorithm is studied using an artificial EEG database, and two real EEG databases: a database of two classes of EEGs elicited during a task of imagery of hand upward and downward movements of a computer screen cursor (db Ia), and a database of two classes of sensorimotor EEGs elicited during a feedback-regulated left–right motor imagery task (db III). The results show that the proposed algorithm outperforms other commonly used methods with classification rate improvement of 2 and 10% for db Ia and db III, respectively. In addition, the proposed method outperforms a support vector machine classifier with a linear kernel, when both classifiers utilize the same feature set. The results also show that a model architecture which includes a left-to-right scheme with no skips, five states and three Gaussians, outperforms the other tested architectures due to the fact that it allows a better modeling of the temporal sequencing of the EEG components.

The incidence of thyroid cancer is rising faster than other malignancies and has nearly doubled in the United States (U.S.) in the last 30 years. However, classifying between malignant and benign thyroid nodules is often difficult. Although ultrasound guided Fine Needle Aspiration Biopsy (FNAB) is considered an excellent tool for triaging patients, up to 25% of FNABs are inconclusive. As a result, definitive diagnosis requires an exploratory surgery and a large number of these are performed in the U.S. annually. It would be extremely beneficial to develop a non-invasive tool or procedure that could assist in assessing the likelihood of malignancy of otherwise indeterminate thyroid nodules, thereby reducing the number of exploratory thyroidectomies that are performed under general anesthesia. In this preliminary study we demonstrate a unique hand-held Resonance-frequency based Electrical Impedance Spectroscopy (REIS) device with six pairs of detection probes to detect and classify thyroid nodules using multi-channel EIS output signal sweeps. Under an Institutional Review Board (IRB)-approved case collection protocol, this REIS device is being tested in our clinical facility and we have been collecting an initial patient data set since March of this year. Between March and August of 2011, 65 EIS tests were conducted on 65 patients. Among these cases, six depicted pathology-verified malignant cells. Our initial assessment indicates the feasibility of easily applying this REIS device and measurement approach in a very busy clinical setting. The measured resonance frequency differences between malignant and benign nodules could potentially make it possible to accurately classify indeterminate thyroid nodules.

This study developed and assessed a computerized scheme to detect breast abnormalities and predict the risk of developing cancer based on bilateral mammographic tissue asymmetry. A digital mammography database of 100 randomly selected negative cases and 100 positive cases for having high-risk of developing breast cancer was established. Each case includes four images of cranio-caudal (CC) and medio-lateral oblique (MLO) views of the left and right breast. To detect bilateral mammographic tissue asymmetry, a pool of 20 computed features was assembled. A genetic algorithm was applied to select optimal features and build an artificial neural network based classifier to predict the likelihood of a test case being positive. The leave-one-case-out validation method was used to evaluate the classifier performance. Several approaches were investigated to improve the classification performance including extracting asymmetrical tissue features from either selected regions of interests or the entire segmented breast area depicted on bilateral images in one view, and the fusion of classification results from two views. The results showed that (1) using the features computed from the entire breast area, the classifier yielded the higher performance than using ROIs, and (2) using a weighted average fusion method, the classifier achieved the highest performance with the area under ROC curve of 0.781±0.023. At 90% specificity, the scheme detected 58.3% of high-risk cases in which cancers developed and verified 6-18 months later. The study demonstrated the feasibility of applying a computerized scheme to detect cases with high risk of developing breast cancer based on computer-detected bilateral mammographic tissue asymmetry.

In this study we present a texture-based method of emphysema segmentation depicted on CT examination consisting of two steps. Step 1, a fractal dimension based texture feature extraction is used to initially detect base regions of emphysema. A threshold is applied to the texture result image to obtain initial base regions. Step 2, the base regions are evaluated pixel-by-pixel using a method that considers the variance change incurred by adding a pixel to the base in an effort to refine the boundary of the base regions. Visual inspection revealed a reasonable segmentation of the emphysema regions. There was a strong correlation between lung function (FEV1%, FEV1/FVC, and DLCO%) and fraction of emphysema computed using the texture based method, which were -0.433, -.629, and -0.527, respectively. The texture-based method produced more homogeneous emphysematous regions compared to simple thresholding, especially for large bulla, which can appear as speckled regions in the threshold approach. In the texture-based method, single isolated pixels may be considered as emphysema only if neighboring pixels meet certain criteria, which support the idea that single isolated pixels may not be sufficient evidence that emphysema is present. One of the strength of our complex texture-based approach to emphysema segmentation is that it goes beyond existing approaches that typically extract a single or groups texture features and individually analyze the features. We focus on first identifying potential regions of emphysema and then refining the boundary of the detected regions based on texture patterns.