Automatic Tumor Detection Research Articles

Automated Cancer Detection Using CNNs: Innovations and Applications

Large-scale datasets and complicated cell architectures provide challenges for traditional tumor identification approaches, which often rely on human analysis or traditional machine-learning techniques. This may result in errors and inefficiencies. The inconsistent shape of tumor cells may be a challenge for these methods, making border detection and classification inconsistent. This paper suggests an automated tumor detection technique based on convolutional neural networks (CNNs) to increase tumor cell border detection and classification accuracy and efficiency. Several experiments were carried out to confirm the suggested strategy's efficacy. This entails developing and refining the CNN architecture in addition to merging deep learning and data augmentation methods. According to the findings, the model has performed well in a variety of experimental settings, especially when it comes to classifying complicated cell samples. Furthermore, the feasibility of using this model for real-time border detection was explored, indicating its suitability for use in a medical setting. Subsequent investigations will concentrate on enhancing the capabilities of models, expanding their range, and tackling problems like disparities in data. This discovery is significant because it has the potential to transform tumor identification by providing a more accurate, scalable, and efficient approach that might be used extensively in clinical settings and eventually lead to better patient outcomes.

Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images.

Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance WSIs containing small tumors where the tumor may include only a few isolated cells. For early detection, it is important that MIL algorithms can identify small tumors. Existing studies have attempted to address this issue using attention-based architectures and instance selection-based methodologies but have not produced significant improvements. This paper proposes crossattention-based salient instance inference MIL (CASiiMIL), which involves a novel saliency-informed attention mechanism to identify small tumors (e.g., breast cancer lymph node micro-metastasis) on WSIs without needing any annotations. In addition to this new attention mechanism, we introduce a negative representation learning algorithm to facilitate the learning of saliencyinformed attention weights for improved sensitivity on tumor WSIs. The proposed model outperforms the state-ofthe-art MIL methods on two popular tumor metastasis detection datasets. The proposed approach demonstrates great cross-center generalizability, high accuracy in classifying WSIs with small tumor lesions, and excellent interpretability attributed to the saliency-informed attention weights. We expect that the proposed method will pave the way for training algorithms for early tumor detection on large datasets where acquiring fine-grained annotations is is not practical.

An artificial intelligence-based recognition model of colorectal liver metastases in intraoperative ultrasonography with improved accuracy through algorithm integration.

Contrast-enhanced intraoperative ultrasonography (CE-IOUS) is crucial for detecting colorectal liver metastases (CLM) during surgery. Although artificial intelligence shows potential in diagnostic systems, its application in CE-IOUS is limited. This study aimed to develop an automatic tumor detection model using Mask region-based convolutional neural network (Mask R-CNN) for CE-IOUS images. CE-IOUS videos of the CLM from 121 patients were collected, generating ground truth data. A total of 2659 images were obtained. Two models were developed: the basic recognition model (BRM), which was trained on CE-mode images, and the subtraction model (SM), which used images created by a subtraction algorithm that highlighted the differences in pixel values between the basic-mode and CE-mode images. The subtraction algorithm focuses on echogenicity differences. These two models were combined into a combination model (CM), which assessed outcomes using the prediction probabilities from both models. The optimal epochs were determined by the maximum area under the curve (AUC), and the thresholds were calculated accordingly. BRM, SM, and CM achieved 89.4%, 86.6%, and 96.5% accuracy, respectively. CM outperformed the individual models, achieving an AUC of 0.99. A novel automated recognition model was developed for accurate CLM detection in CE-IOUS by integrating image- and algorithm-based models.

Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images

Breast cancer (BC) is the most common type of cancer among women globally and is one of the leading causes of cancer-related deaths among women. In the diagnosis of BC, histopathological assessment is the gold standard, where automated tumor detection technologies play a pivotal role. Utilizing Convolutional Neural Networks (CNNs) for automated analysis of image patches from Whole Slide Images (WSIs) enhances detection accuracy and alleviates the workload of pathologists. However, CNNs often face limitations in handling pathological patches due to a lack of sufficient contextual information and limited feature generation capabilities. To address this, we propose a novel Multi-scale Multi-head Self-attention Ensemble Network (MMSEN), which integrates a multi-scale feature generation module, a convolutional self-attention module, and an adaptive feature integration with an output module, effectively optimizing the performance of classical CNNs. The design of MMSEN optimizes the capture of key information and the comprehensive integration of features in WSIs pathological patches, significantly enhancing the precision of tumor detection. Validation results from a five-fold cross-validation experiment on the PatchCamelyon (PCam) dataset demonstrate that MMSEN achieves a ROC-AUC of 99.01% ± 0.02%, an F1-score of 98.00% ± 0.08%, a Balanced Accuracy (B-Acc) of 98.00% ± 0.08%, and a Matthews Correlation Coefficient (MCC) of 96.00% ± 0.16% (p<0.05). These results demonstrate the effectiveness and potential of MMSEN in detecting tumors from pathological patches in WSIs for BC.

MDER-Net: A Multi-Scale Detail-Enhanced Reverse Attention Network for Semantic Segmentation of Bladder Tumors in Cystoscopy Images

White light cystoscopy is the gold standard for the diagnosis of bladder cancer. Automatic and accurate tumor detection is essential to improve the surgical resection of bladder cancer and reduce tumor recurrence. At present, Transformer-based medical image segmentation algorithms face challenges in restoring fine-grained detail information and local boundary information of features and have limited adaptability to multi-scale features of lesions. To address these issues, we propose a new multi-scale detail-enhanced reverse attention network, MDER-Net, for accurate and robust bladder tumor segmentation. Firstly, we propose a new multi-scale efficient channel attention module (MECA) to process four different levels of features extracted by the PVT v2 encoder to adapt to the multi-scale changes in bladder tumors; secondly, we use the dense aggregation module (DA) to aggregate multi-scale advanced semantic feature information; then, the similarity aggregation module (SAM) is used to fuse multi-scale high-level and low-level features, complementing each other in position and detail information; finally, we propose a new detail-enhanced reverse attention module (DERA) to capture non-salient boundary features and gradually explore supplementing tumor boundary feature information and fine-grained detail information; in addition, we propose a new efficient channel space attention module (ECSA) that enhances local context and improves segmentation performance by suppressing redundant information in low-level features. Extensive experiments on the bladder tumor dataset BtAMU, established in this article, and five publicly available polyp datasets show that MDER-Net outperforms eight state-of-the-art (SOTA) methods in terms of effectiveness, robustness, and generalization ability.

Deep learning for the automatic detection and segmentation of parotid gland tumors on MRI

ObjectivesParotid gland tumors (PGTs) often occur as incidental findings on magnetic resonance images (MRI) that may be overlooked. This study aimed to construct and validate a deep learning model to automatically identify parotid glands (PGs) with a PGT from normal PGs, and in those with a PGT to segment the tumor. Materials and methodsThe nnUNet combined with a PG-specific post-processing procedure was used to develop the deep learning model trained on T1-weighed images (T1WI) in 311 patients (180 PGs with tumors and 442 normal PGs) and fat-suppressed (FS)-T2WI in 257 patients (125 PGs with tumors and 389 normal PGs), for detecting and segmenting PGTs with five-fold cross-validation. Additional validation set separated by time, comprising T1WI in 34 and FS-T2WI in 41 patients, was used to validate the model performance. Results and conclusionTo identify PGs with tumors from normal PGs, using combined T1WI and FS-T2WI, the deep learning model achieved an accuracy, sensitivity and specificity of 98.2% (497/506), 100% (119/119) and 97.7% (378/387), respectively, in the cross-validation set and 98.5% (67/68), 100% (20/20) and 97.9% (47/48), respectively, in the validation set. For patients with PGTs, automatic segmentation of PGTs on T1WI and FS-T2WI achieved mean dice coefficients of 86.1% and 84.2%, respectively, in the cross-validation set, and of 85.9% and 81.0%, respectively, in the validation set. The proposed deep learning model may assist the detection and segmentation of PGTs and, by acting as a second pair of eyes, ensure that incidentally detected PGTs on MRI are not missed.

HM_ADET: a hybrid model for automatic detection of eyelid tumors based on photographic images

BackgroundThe accurate detection of eyelid tumors is essential for effective treatment, but it can be challenging due to small and unevenly distributed lesions surrounded by irrelevant noise. Moreover, early symptoms of eyelid tumors are atypical, and some categories of eyelid tumors exhibit similar color and texture features, making it difficult to distinguish between benign and malignant eyelid tumors, particularly for ophthalmologists with limited clinical experience.MethodsWe propose a hybrid model, HM_ADET, for automatic detection of eyelid tumors, including YOLOv7_CNFG to locate eyelid tumors and vision transformer (ViT) to classify benign and malignant eyelid tumors. First, the ConvNeXt module with an inverted bottleneck layer in the backbone of YOLOv7_CNFG is employed to prevent information loss of small eyelid tumors. Then, the flexible rectified linear unit (FReLU) is applied to capture multi-scale features such as texture, edge, and shape, thereby improving the localization accuracy of eyelid tumors. In addition, considering the geometric center and area difference between the predicted box (PB) and the ground truth box (GT), the GIoU_loss was utilized to handle cases of eyelid tumors with varying shapes and irregular boundaries. Finally, the multi-head attention (MHA) module is applied in ViT to extract discriminative features of eyelid tumors for benign and malignant classification.ResultsExperimental results demonstrate that the HM_ADET model achieves excellent performance in the detection of eyelid tumors. In specific, YOLOv7_CNFG outperforms YOLOv7, with AP increasing from 0.763 to 0.893 on the internal test set and from 0.647 to 0.765 on the external test set. ViT achieves AUCs of 0.945 (95% CI 0.894-0.981) and 0.915 (95% CI 0.860-0.955) for the classification of benign and malignant tumors on the internal and external test sets, respectively.ConclusionsOur study provides a promising strategy for the automatic diagnosis of eyelid tumors, which could potentially improve patient outcomes and reduce healthcare costs.

A novel approach for automatic tumor detection and localization in mammography images via mixture of factor analyzers based on co-clustering

This paper proposes a novel model-based method called the Extension of Mixture of Factor Analyzers for Co-Clustering of Images (EMFACCI) to segment grayscale mammography images into a number of blocks, one of which contains the tumor. After image preprocessing and enhancement, the EMFACCI model first determines the optimal number of row clusters and column clusters by reducing the local dimension. Using the optimal number of blocks, the proposed model changes the location of columns and rows by clustering them simultaneously until it identifies the block containing the tumor. Finally, the detected block is binarized using Fuzzy C-Mean clustering and located on the input image by the proposed model, while other blocks are removed. The performance of the proposed method is evaluated on images taken from the MIAS and DDSM datasets. The results demonstrate the effectiveness of the proposed method based on sensitivity, specificity, dice, jaccard similarity index (JSI), false-negative rate (FNR), false-positive rate (FPR), and accuracy. Our proposed method performs well in detecting tumors of different sizes and is accurate in dealing with complex and high-dimensional images. Additionally, it outperforms existing detection methods.

Automatic detection, segmentation, and classification of primary bone tumors and bone infections using an ensemble multi-task deep learning framework on multi-parametric MRIs: a multi-center study.

To develop an ensemble multi-task deep learning (DL) framework for automatic and simultaneous detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections based on multi-parametric MRI from multi-center. This retrospective study divided 749 patients with PBTs or bone infections from two hospitals into a training set (N = 557), an internal validation set (N = 139), and an external validation set (N = 53). The ensemble framework was constructed using T1-weighted image (T1WI), T2-weighted image (T2WI), and clinical characteristics for binary (PBTs/bone infections) and three-category (benign/intermediate/malignant PBTs) classification. The detection and segmentation performances were evaluated using Intersection over Union (IoU) and Dice score. The classification performance was evaluated using the receiver operating characteristic (ROC) curve and compared with radiologist interpretations. On the external validation set, the single T1WI-based and T2WI-based multi-task models obtained IoUs of 0.71 ± 0.25/0.65 ± 0.30 for detection and Dice scores of 0.75 ± 0.26/0.70 ± 0.33 for segmentation. The framework achieved AUCs of 0.959 (95%CI, 0.955-1.000)/0.900 (95%CI, 0.773-0.100) and accuracies of 90.6% (95%CI, 79.7-95.9%)/78.3% (95%CI, 58.1-90.3%) for the binary/three-category classification. Meanwhile, for the three-category classification, the performance of the framework was superior to that of three junior radiologists (accuracy: 65.2%, 69.6%, and 69.6%, respectively) and comparable to that of two senior radiologists (accuracy: 78.3% and 78.3%). The MRI-based ensemble multi-task framework shows promising performance in automatically and simultaneously detecting, segmenting, and classifying PBTs and bone infections, which was preferable to junior radiologists. Compared with junior radiologists, the ensemble multi-task deep learning framework effectively improves differential diagnosis for patients with primary bone tumors or bone infections. This finding may help physicians make treatment decisions and enable timely treatment of patients. • The ensemble framework fusing multi-parametric MRI and clinical characteristics effectively improves the classification ability of single-modality models. • The ensemble multi-task deep learning framework performed well in detecting, segmenting, and classifying primary bone tumors and bone infections. • The ensemble framework achieves an optimal classification performance superior to junior radiologists' interpretations, assisting the clinical differential diagnosis of primary bone tumors and bone infections.

A hyperspectral index-based approach for in vivo automatic detection of skin tumors from hyperspectral images

Skin cancer is the most common human malignancy and early diagnosis is important for successful treatment. Hyperspectral imaging provides both spectral and spatial information which may be useful for tumor diagnosis. In the present study, hyperspectral images of 36 skin tumors (seborrheic keratosis, actinic keratosis, basal cell carcinoma, squamous cell carcinoma) were acquired and analyzed. A skin cancer index was defined taking into account the differences in the angles made by the spectral slopes of the tumors and normal skin and the x axis in the spectral range (580-600) nm (where the differences in spectral signatures proved to be significant). When compared to histopathological diagnosis, the index allowed for differentiation between benign and malignant tumors and even between seborrheic and actinic keratosis or basal cell carcinoma and squamous cell carcinoma. Benign tumors had an index under 0.39999, while basal cell carcinoma ranged between 0.4000-0.59999, and squamous cell carcinoma between 0.6000-06999. In conclusion, the hyperspectral image-based skin tumor index is an objective method of identification and differentiation of skin tumors and may become a useful tool in their early diagnosis

A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid.

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies.

Automatic liver tumor detection and classification using the hyper tangent fuzzy C-Means and improved fuzzy SVM

Automatic liver tumor detection and classification using the hyper tangent fuzzy C-Means and improved fuzzy SVM

High-resolution full-field optical coherence tomography microscope for the evaluation of freshly excised skin specimens during Mohs surgery: A feasibility study.

Histopathology for tumor margin assessment is time-consuming and expensive. High-resolution full-field optical coherence tomography (FF-OCT) images fresh tissues rapidly at cellular resolution and potentially facilitates evaluation. Here, we define FF-OCT features of normal and neoplastic skin lesions in fresh ex vivo tissues and assess its diagnostic accuracy for malignancies. For this, normal and neoplastic tissues were obtained from Mohs surgery, imaged using FF-OCT, and their features were described. Two expert OCT readers conducted a blinded analysis to evaluate their diagnostic accuracies, using histopathology as the ground truth. A convolutional neural network was built to distinguish and outline normal structures and tumors. Of the 113 tissues imaged, 95 (84%) had a tumor (75 basal cell carcinomas [BCCs] and 17 squamous cell carcinomas [SCCs]). The average reader diagnostic accuracy was 88.1%, with a sensitivity of 93.7%, and a specificity of 58.3%. The artificial intelligence (AI) model achieved a diagnostic accuracy of 87.6 ± 5.9%, sensitivity of 93.2 ± 2.1%, and specificity of 81.2 ± 9.2%. A mean intersection-over-union of 60.3 ± 10.1% was achieved when delineating the nodular BCC from normal structures. Limitation of the study was the small sample size for all tumors, especially SCCs. However, based on our preliminary results, we envision FF-OCT to rapidly image fresh tissues, facilitating surgical margin assessment. AI algorithms can aid in automated tumor detection, enabling widespread adoption of this technique.

Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN.

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.

Tumor detection under cystoscopy with transformer-augmented deep learning algorithm

Objective. Accurate tumor detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. Advanced deep learning algorithms hold the potential to improve the performance of standard white-light cystoscopy (WLC) in a noninvasive and cost-effective fashion. The purpose of this work is to develop a cost-effective, transformer-augmented deep learning algorithm for accurate detection of bladder tumors in WLC and to assess its performance on archived patient data. Approach. ‘CystoNet-T’, a deep learning-based bladder tumor detector, was developed with a transformer-augmented pyramidal CNN architecture to improve automated tumor detection of WLC. CystoNet-T incorporated the self-attention mechanism by attaching transformer encoder modules to the pyramidal layers of the feature pyramid network (FPN), and obtained multi-scale activation maps with global features aggregation. Features resulting from context augmentation served as the input to a region-based detector to produce tumor detection predictions. The training set was constructed by 510 WLC frames that were obtained from cystoscopy video sequences acquired from 54 patients. The test set was constructed based on 101 images obtained from WLC sequences of 13 patients. Main results. CystoNet-T was evaluated on the test set with 96.4 F1 and 91.4 AP (Average Precision). This result improved the benchmark of Faster R-CNN and YOLO by 7.3 points in F1 and 3.8 points in AP. The improvement is attributed to the strong ability of global attention of CystoNet-T and better feature learning of the pyramids architecture throughout the training. The model was found to be particularly effective in highlighting the foreground information for precise localization of the true positives while favorably avoiding false alarms Significance. We have developed a deep learning algorithm that accurately detects bladder tumors in WLC. Transformer-augmented AI framework promises to aid in clinical decision-making for improved bladder cancer diagnosis and therapeutic guidance.

Immunonutrition supplementation for resectable gastric cancer during standard perioperative chemotherapy (CT) FLOT: A pivotal study, I–SUPPLY.

57 Background: Gastric carcinomas (GC) can be divided into immunogenic and immune-resistant based on the infiltration of the tumour microenvironment (TME) from different immune cells (ICs) such as Natural Killer (NK), activated CD8+, CD3+ and CD4+ memory T cells. High ImmunoScore (IS) (positive cells/mm2 for each marker (CD8+, CD3+, Foxp3+) in the Core Tumour and Invasive Margins) is strongly related to better prognosis of GC patients in terms of Disease-Free Survival and Overall Survival. ICs activation and consequent immunogenic TME requires high nutrient absorption and synthesis accumulation of protein, lipid and nucleotides. However, tumour cells (TCs) promote their own proliferation by stealing the available micronutrients to ICs therefore leading to an immunosuppressive TME phenotype. Methods: This is a translational, prospective, non-randomized study. Primary endpoint is to assess if enteral immune nutrition (EIN) supplementation during the acme of the activity of cytotoxic of perioperative FLOT (docetaxel, oxaliplatin and 5-fluorouracil) in stage II-III GC can revert the TME in favour of ICs over TCs, witnessed by an increase of IS in surgical specimen over biopsy controls. A steady amount of EIN with Arginine, Omega-3-fatty acids, Glutamine and ribonucleic acid, will be added to patient’s diet from day 3 to 8 of each FLOT cycle, twice a day. We plan to use Image analysis software for automatic tumor detection and quantification of CD3+ and CD8+ stained cells whose densities will be converted into IS with pre-defined cutoffs. IS utilizes standardized percentile values (0 to 100%). We identify IS as Low, Intermediate (Int) and High with a mean percentile of 0-25%, &gt;25-70%, &gt;70-100% respectively. Two-group classifications are planned, corresponding to IS Low and Int-High. To better define the kind of tumor infiltrating cells (TILs) and assess the amount of immune suppressive cells, more IHC scoring will be evaluated: helper T-cells CD4+, CD8 T memory (CD 103+), T regulatory (both CD4+ and Foxp3+/CD105+), Macrophages M1 (CD 68+) and M2 (CD 163+). Results: Based on historical controls the rate of Int-High IS (&gt;25-70%, &gt;70% respectively) is about 64%, we expect to find a 20% increase of Int-High IS GC over biopsy control (internal validation cohort). To detect an increase in the percentage of high IS among patients treated with EIN (estimated around 85%) compared to biopsy controls (estimated around 64%) assuming a probability α and β of 0.2, the required sample size will be of 21 patients with resectable GC receiving EIN supplementation plus standard CT. To define the sample size a single proportion test was used. Conclusions: Our hypothesis is that EIN given during perioperative CT might prevent the establishment of an unfavourable TME for ICs in both auxotrophic and non-auxotrophic malignancies, thus leading to an increase of GC immunogenic pattern.

Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion.

Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited. However, most anomaly localization research in machine learning focuses on non-medical datasets, and we find that these methods fall short when adapted to medical imaging datasets. The problem is alleviated when we solve the task from the image completion perspective, in which the presence of anomalies can be indicated by a discrepancy between the original appearance and its auto-completion conditioned on the surroundings. However, there are often many valid normal completions given the same surroundings, especially in the DBT dataset, making this evaluation criterion less precise. To address such an issue, we consider pluralistic image completion by exploring the distribution of possible completions instead of generating fixed predictions. This is achieved through our novel application of spatial dropout on the completion network during inference time only, which requires no additional training cost and is effective at generating diverse completions. We further propose minimum completion distance (MCD), a new metric for detecting anomalies, thanks to these stochastic completions. We provide theoretical as well as empirical support for the superiority over existing methods of using the proposed method for anomaly localization. On the DBT dataset, our model outperforms other state-of-the-art methods by at least 10% AUROC for pixel-level detection.

NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images

The early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach is not without its flaws. To address the need for early detection, multiple-instance learning (MIL) has emerged as the preferred deep learning method for automatic tumor detection on whole slide images (WSIs). However, existing methods often fail to identify some small lesions due to insufficient attention to small regions. Attention-based multiple-instance learning (ABMIL)-based methods can be particularly problematic because they may focus too much on normal regions, leaving insufficient attention for small-tumor lesions. In this paper, we propose a new ABMIL-based model called normal representative keyset ABMIL (NRK-ABMIL), which addresseses this issue by adjusting the attention mechanism to give more attention to lesions. To accomplish this, the NRK-ABMIL creates an optimal keyset of normal patch embeddings called the normal representative keyset (NRK). The NRK roughly represents the underlying distribution of all normal patch embeddings and is used to modify the attention mechanism of the ABMIL. We evaluated NRK-ABMIL on the publicly available Camelyon16 and Camelyon17 datasets and found that it outperformed existing state-of-the-art methods in accurately identifying small tumor lesions that may spread over a few patches. Additionally, the NRK-ABMIL also performed exceptionally well in identifying medium/large tumor lesions.

ELCT-YOLO: An Efficient One-Stage Model for Automatic Lung Tumor Detection Based on CT Images

Research on lung cancer automatic detection using deep learning algorithms has achieved good results but, due to the complexity of tumor edge features and possible changes in tumor positions, it is still a great challenge to diagnose patients with lung tumors based on computed tomography (CT) images. In order to solve the problem of scales and meet the requirements of real-time detection, an efficient one-stage model for automatic lung tumor detection in CT Images, called ELCT-YOLO, is presented in this paper. Instead of deepening the backbone or relying on a complex feature fusion network, ELCT-YOLO uses a specially designed neck structure, which is suitable to enhance the multi-scale representation ability of the entire feature layer. At the same time, in order to solve the problem of lacking a receptive field after decoupling, the proposed model uses a novel Cascaded Refinement Scheme (CRS), composed of two different types of receptive field enhancement modules (RFEMs), which enables expanding the effective receptive field and aggregate multi-scale context information, thus improving the tumor detection performance of the model. The experimental results show that the proposed ELCT-YOLO model has strong ability in expressing multi-scale information and good robustness in detecting lung tumors of various sizes.

Fully-automated detection of small bowel carcinoid tumors in CT scans using deep learning.

Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician awareness. Computed tomography (CT) is the most common imaging study for diagnosis of small bowel carcinoid tumor. It is often used with positron emission tomography (PET) to capture anatomical and functional aspects of carcinoid tumors and thus to increase thesensitivity. We compared three different kinds of methods for the automatic detection of small bowel carcinoid tumors on CT scans, which is the first to the best of ourknowledge. Thirty-three preoperative CT scans of 33 unique patients with surgically-proven carcinoid tumors within the small bowel were collected. Ground-truth segmentation of tumors was drawn on CT scans by referring to available 18 F-DOPA PET scans and the corresponding radiology report. These scans were split into the trainval set (n = 24) and the test positive set (n= 9). Additionally, 22 CT scans of 22 unique patients who had no evidence of the tumor were collected to comprise the test negative set. We compared three different kinds of detection methods, which are detection network, patch-based classification, and segmentation-based methods. We also investigated the usefulness of small bowel segmentation for reduction of false positives (FPs) for each method. Free-response receiver operating characteristic (FROC) curves and receiver operating characteristic (ROC) curves were used for lesion- and patient-level evaluations, respectively. Statistical analyses comparing the FROC and ROC curves were alsoperformed. The detection network method performed the best among the compared methods. For lesion-level detection, the detection network method, without the small bowel segmentation-based filtering, achieved sensitivity values of (60.8%, 81.1%, 82.4%, 86.5%) at per-scan FP rates of (1, 2, 4 ,8), respectively. The use of the small bowel segmentation did not improve the performance ( ). For patient-level detection, again the detection network method, but with the small bowel segmentation-based filtering, achieved the highest AUC of 0.86 with a sensitivity of 78% and specificity of 82% at the Youdenpoint. The carcinoid tumors in this patient population were very small and potentially difficult to diagnose. The presented method showed reasonable sensitivity at small numbers of FPs for lesion-level detection. It also achieved a promising AUC for patient-level detection. The method may have clinical application in patients with this rare and difficult to detectdisease.