Search Results for « Diagnosis »

Cognitive diagnosis (CD) aims to reveal students' proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students' knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy by analyzing students' explicit response records, they primarily focus on individual knowledge state, failing to adequately reflect the relative ability performance of students within hierarchies. To address this, we propose the Hierarchy Constraint-Aware Cognitive Diagnosis Framework (HCD), designed to more accurately represent student ability performance within real educational contexts. Specifically, the framework introduces a hierarchy mapping layer to identify students' levels. It then employs a hierarchy convolution-enhanced attention layer for in-depth analysis of knowledge concepts performance among students at the same level, uncovering nuanced differences. A hierarchy inter-sampling attention layer captures performance differences across hierarchies, offering a comprehensive understanding of the relationships among students' knowledge state. Finally, through personalized diagnostic enhancement, the framework integrates hierarchy constraint perception features with existing models, improving the representation of both individual and group characteristics. This approach enables precise inference of students' knowledge state. Research shows that this framework not only reasonably constrains changes in students' knowledge states to align with real educational settings, but also supports the scientific rigor and fairness of educational assessments, thereby advancing the field of cognitive diagnosis.

Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fracture from elbow X-ray images by integrating domain-specific medical knowledge into a curriculum learning framework. In our method, the training data are permutated by sampling without replacement at the beginning of each training epoch. The sampling probability of each training sample is guided by a scoring criterion constructed based on clinically known knowledge from human experts, where the scoring indicates the diagnosis difficultness of different elbow fracture subtypes. We also propose an algorithm that updates the sampling probabilities at each epoch, which is applicable to other sampling-based curriculum learning frameworks. We design an experiment with 1865 elbow X-ray images for a fracture/normal binary classification task and compare our proposed method to a baseline method and a previous method using multiple metrics. Our results show that the proposed method achieves the highest classification performance. Also, our proposed probability update algorithm boosts the performance of the previous method.

Abstract In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of prenatal diagnosis. Each bibliography is divided into 22 sections: 1 Reviews; 2 General Interest; 3 Normal Fetal Development; 4 Preimplantation Genetic Diagnosis; 5 First Trimester Diagnosis; 6 Second Trimester Diagnosis; 7 Fetal Imaging: General; Ultrasound; MRI; Doppler; 8 Maternal Serum Screening for Aneuploidy; 9 Screening for Carriers of Genetic Abnormality; 10 Molecular Cytogenetics: Metaphase Cytogenetics/FISH; Array CGH; 11 Fetal Cells in Maternal Circulation; 12 Fetal DNA/RNA in Maternal Body Fluids; 13 Fetal Therapy; 14 Psychosocial and Ethical Aspects; 15 Epidemiology and Environmental Factors; 16 Developmental and Placental Pathology; 17 Genetic Counseling. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted

In the realm of intelligent education, cognitive diagnosis plays a crucial role in subsequent recommendation tasks attributed to the revealed students' proficiency in knowledge concepts. Although neural network-based neural cognitive diagnosis models (CDMs) have exhibited significantly better performance than traditional models, neural cognitive diagnosis is criticized for the poor model interpretability due to the multi-layer perception (MLP) employed, even with the monotonicity assumption. Therefore, this paper proposes to empower the interpretability of neural cognitive diagnosis models through efficient kolmogorov-arnold networks (KANs), named KAN2CD, where KANs are designed to enhance interpretability in two manners. Specifically, in the first manner, KANs are directly used to replace the used MLPs in existing neural CDMs; while in the second manner, the student embedding, exercise embedding, and concept embedding are directly processed by several KANs, and then their outputs are further combined and learned in a unified KAN to get final predictions. To overcome the problem of training KANs slowly, we modify the implementation of original KANs to accelerate the training. Experiments on four real-world datasets show that the proposed KA2NCD exhibits better performance than traditional CDMs, and the proposed KA2NCD still has a bit of performance leading even over the existing neural CDMs. More importantly, the learned structures of KANs enable the proposed KA2NCD to hold as good interpretability as traditional CDMs, which is superior to existing neural CDMs. Besides, the training cost of the proposed KA2NCD is competitive to existing models.

Abstract In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of prenatal diagnosis. Each bibliography is divided into 22 sections: 1 Reviews; 2 General Interest; 3 Normal Fetal Development; 4 Preimplantation Genetic Diagnosis; 5 First Trimester Diagnosis; 6 Second Trimester Diagnosis; 7 Fetal Imaging: General; Ultrasound; MRI; Doppler; 8 Maternal Serum Screening for Aneuploidy; 9 Screening for Carriers of Genetic Abnormality; 10 Molecular Cytogenetics: Metaphase Cytogenetics/FISH; Array CGH; 11 Fetal Cells in Maternal Circulation; 12 Fetal DNA/RNA in Maternal Body Fluids; 13 Fetal Therapy; 14 Psychosocial and Ethical Aspects; 15 Epidemiology and Environmental Factors; 16 Developmental and Placental Pathology; 17 Genetic Counseling. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted

Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also leads to explainable and verbose findings that can assist the radiologists in the final and increase trust. This work identifies the concepts networks use for vertebral fracture diagnosis in CT images. This is achieved by associating concepts to neurons highly correlated with a specific diagnosis in the dataset. The concepts are either associated with neurons by radiologists pre-hoc or are visualized during a specific prediction and left for the user's interpretation. We evaluate which concepts lead to correct diagnosis and which concepts lead to false positives. The proposed frameworks and analysis pave the way for reliable and explainable vertebral fracture diagnosis.

Abstract In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of prenatal diagnosis. Each bibliography is divided into 22 sections: 1 Reviews; 2 General Interest; 3 Normal Fetal Development; 4 Preimplantation Genetic Diagnosis; 5 First Trimester Diagnosis; 6 Second Trimester Diagnosis; 7 Fetal Imaging: General; Ultrasound; MRI; Doppler; 8 Maternal Serum Screening for Aneuploidy; 9 Screening for Carriers of Genetic Abnormality; 10 Molecular Cytogenetics: Metaphase Cytogenetics/FISH; Array CGH; 11 Fetal Cells in Maternal Circulation; 12 Fetal DNA/RNA in Maternal Body Fluids; 13 Fetal Therapy; 14 Psychosocial and Ethical Aspects; 15 Epidemiology and Environmental Factors; 16 Developmental and Placental Pathology; 17 Genetic Counseling. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.

Abstract In order to keep subscribers up‐to‐date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of prenatal diagnosis. Each bibliography is divided into 22 sections: 1 Reviews; 2 General Interest; 3 Normal Fetal Development; 4 Preimplantation Genetic Diagnosis; 5 First Trimester Diagnosis; 6 Second Trimester Diagnosis; 7 Fetal Imaging: General; Ultrasound; MRI; Doppler; 8 Maternal Serum Screening for Aneuploidy; 9 Screening for Carriers of Genetic Abnormality; 10 Molecular Cytogenetics: Metaphase Cytogenetics/FISH; Array CGH; 11 Fetal Cells in Maternal Circulation; 12 Fetal DNA/RNA in Maternal Body Fluids; 13 Fetal Therapy; 14 Psychosocial and Ethical Aspects; 15 Epidemiology and Environmental Factors; 16 Developmental and Placental Pathology; 17 Genetic Counseling. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted