Breast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening a...
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ISBN:
(纸本)9798350311075
Breast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast cancer. the design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially at pixel level, the large size of the images with relatively small cancer lesion sizes and class imbalance, a wide diversity of cancerous lesions, a variety of breasts, both in size and density, make the training of the neural models challenging. Moreover, clinicians are often concerned about using these black-box models because of the lack of transparency in their inference. To address these issues, we propose an approach taking advantage of Multiple Instance Learning (MIL), supported by attention mechanisms. We researched Attention-based MIL (AMIL), Gated AMIL (GAMIL), Dual Stream MIL (DSMIL) and CLustering-constrained AMIL (CLAM) models trained in a weakly-supervised manner and compared them with a common model in image classification tasks, ResNet18. the approach described in this paper is multimodal and combines two mammographic projections (CC and MLO) in the training process. the developed neural system achieved high classification efficiency. Furthermore, exploiting the generated attentional maps allowed the localisation of cancerous lesions, thus increasing the interpretability of the algorithm. thanks to this mechanism, we were also able to detect artifacts in the analyzed database, difficult to spot but drastically skewing the algorithm's performance.
this paper discusses the modelling and simulation of a reconfigurable wheelchair with a sit-to-stand facility for a disabled 25kg child. the prototype is designed in SolidWorks® and simulated in MATLAB® to g...
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this paper discusses the modelling and simulation of a reconfigurable wheelchair with a sit-to-stand facility for a disabled 25kg child. the prototype is designed in SolidWorks® and simulated in MATLAB® to get the suitable forces that have to be provided by the motors to activate the system. three different techniques will be used in modelling the wheelchair. SolidWorks® software package will be used in the initial stage in order to get an estimation of the necessary motor power. A second model of the system will be built using SimMechanics Matlab toolbox®. In order to check the results obtained from both approaches; SolidWorks and SimMechanics, the model built designed by SolidWorks is embedded in SimMechanics where the system has been simulated for the third time. In all the three simulation stages; the system is considered to do a complete cycle of motion; sit-to-stand and a stand-to-sit. the system performance has been detected based on an open loop scenario where no control is implemented as this stage. Further analysis of the system has been done considering the energy consumption in two different modes; sit-to-stand mode and a forward straight line motion for a prescribed distance.
We present a comparative case study of machine learning models, evaluating their efficiency in a practical task of multiclass classification of samples being submissions to a recruitment survey and assigning them scor...
We present a comparative case study of machine learning models, evaluating their efficiency in a practical task of multiclass classification of samples being submissions to a recruitment survey and assigning them scores denoting the match level for a given candidate to a given workgroup (committee) in the AGH Students’ Council. this research is based on the Council’s recruitment applications that carried candidates’ responses to a set of 10 hypothetical Council member activity scenarios, where they were to choose one of four given solutions to the problems. the data was collected from a web quiz in 2020, validated on a voluntary insider control group’s responses to these questions and finally the best-performing model was evaluated in practice in the 2021’s recruitment inside an in-browser adventure minigame. this work provides insight into how models ranging from classical methods to deep learning perform in a very specific not yet well-explored in literature, practical non-linear problem that is dependent on individual features of the participants, withthe data volume being very limited due to a restricted population of candidates. this information may provide a starting point for applications of machine learning in decision support systems in recruitment processes.
Designing multimodal transportation requires understanding passenger behavior, habits, and needs. the article presents the results of a study aimed at distinguishingthe most frequent combinations of selected modes of ...
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ISBN:
(数字)9798350362343
ISBN:
(纸本)9798350362350
Designing multimodal transportation requires understanding passenger behavior, habits, and needs. the article presents the results of a study aimed at distinguishingthe most frequent combinations of selected modes of transportation from the place of residence to the destination point with an intermediate means such as rail *** data used in the analysis refers to $\mathbf{1 1 0 2}$ passengers of the Lodz Agglomeration Railway (“Łódzka Kolej Aglomeracyjna” sp. z.o.o.), which surveyed passengers in 2021. the survey made it possible to distinguish the most common behaviors among passengers and to identify differences in the choice of means of transportation among the identified groups of passengers. the results can support decision-making in the development of multimodal transportation modes taking into account different passenger behaviors.
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