Enhanced Temporal Correlation for Universal Lesion Detection

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of healthcare.At the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools.This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification(CVOML-SLDC)model.The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images.Primarily,the CVOML-SLDC model derives a gaussian filtering(GF)approach to pre-process the input images and graph cut segmentation is applied.Besides,firefly algorithm(FFA)with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors.Moreover,naïve bayes(NB)classifier is utilized for the skin lesion detection and classification model.The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model.The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset.The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.

Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks

The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the disease.Manual detection of lesions is very time consuming and lacks accuracy.Most of the lesions are difficult to detect manually,especially within the grey matter.This paper proposes a novel and fully automated convolution neural network(CNN)approach to segment lesions.The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly.The first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase efficiency.The system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with CNN.Three routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI artifacts.We evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and ISBI.Quantitative and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art methods.The proposed method shows consistent higher precision and sensitivity than other methods.The proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.

Artificial intelligence fails to improve colonoscopy quality:A single centre retrospective cohort study

BACKGROUND Limited data currently exists on the clinical utility of Artificial Intelligence Assisted Colonoscopy(AIAC)outside of clinical trials.AIM To evaluate the impact of AIAC on key markers of colonoscopy quality compared to conventional colonoscopy(CC).METHODS This single-centre retrospective observational cohort study included all patients undergoing colonoscopy at a secondary centre in Brisbane,Australia.CC outcomes between October 2021 and October 2022 were compared with AIAC outcomes after the introduction of the Olympus Endo-AID module from October 2022 to January 2023.Endoscopists who conducted over 50 procedures before and after AIAC introduction were included.Procedures for surveillance of inflammatory bowel disease were excluded.Patient demographics,proceduralist specialisation,indication for colonoscopy,and colonoscopy quality metrics were collected.Adenoma detection rate(ADR)and sessile serrated lesion detection rate(SSLDR)were calculated for both AIAC and CC.RESULTS The study included 746 AIAC procedures and 2162 CC procedures performed by seven endoscopists.Baseline patient demographics were similar,with median age of 60 years with a slight female predominance(52.1%).Procedure indications,bowel preparation quality,and caecal intubation rates were comparable between groups.AIAC had a slightly longer withdrawal time compared to CC,but the difference was not statistically significant.The introduction of AIAC did not significantly change ADR(52.1%for AIAC vs 52.6%for CC,P=0.91)or SSLDR(17.4%for AIAC vs 18.1%for CC,P=0.44).CONCLUSION The implementation of AIAC failed to improve key markers of colonoscopy quality,including ADR,SSLDR and withdrawal time.Further research is required to assess the utility and cost-efficiency of AIAC for high performing endoscopists.

Two-Stage Lesion Detection Approach Based on Dimension-Decomposition and 3D Context

Lesion detection in Computed Tomography(CT) images is a challenging task in the field of computer-aided diagnosis.An important issue is to locate the area of lesion accurately.As a branch of Convolutional Neural Networks(CNNs),3D Context-Enhanced(3DCE) frameworks are designed to detect lesions on CT scans.The False Positives(FPs) detected in 3DCE frameworks are usually caused by inaccurate region proposals,which slow down the inference time.To solve the above problems,a new method is proposed,a dimension-decomposition region proposal network is integrated into 3DCE framework to improve the location accuracy in lesion detection.Without the restriction of "anchors" on ratios and scales,anchors are decomposed to independent "anchor strings".Anchor segments are dynamically combined in accordance with probability,and anchor strings with different lengths dynamically compose bounding boxes.Experiments show that the accurate region proposals generated by our model promote the sensitivity of FPs and spend less inference time compared with the current methods.

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.