A computer-aided detection system for tissue cell nuclei in histological sections is introduced and validated as part of the Biopsy Analysis Support System (BASS). Cell nuclei are selectively stained with monoclonal antibodies, such as the anti-estrogen receptor antibodies, which are widely applied as part of assessing patient prognosis in breast cancer. The detection system uses a receptive field filter to enhance negatively and positively stained cell nuclei and a squashing function to label each pixel value as belonging to the background or a nucleus. In this study, the detection system assessed all biopsies in an automated fashion. Detection and classification of individual nuclei as well as biopsy grading performance was shown to be promising as compared to that of two experts. Sensitivity and positive predictive value were measured to be 83% and 67.4%, respectively. One major advantage of BASS stems from the fact that the system simulates the assessment procedures routinely employed by human experts; thus it can be used as an additional independent expert. Moreover, the system allows the efficient accumulation of data from large numbers of nuclei in a short time span. Therefore, the potential for accurate quantitative assessments is increased and a platform for more standardized evaluations is provided.

Breast cancer is the most common malignancy affecting the female population in industrialized countries. Prognostic factors, such as steroid receptors visualized in biopsy slides, provide critical information to oncologists regarding the hormonal status of the individual tumors. These factors influence the choice of treatment and help in predicting patient survival and probability of recurrence. The objective of this paper is to introduce a new computer-aided system for the classification of breast cancer nuclei based on neural networks. Currently, medical experts assess steroid receptors in breast cancer biopsy slides mostly manually using four or five level grading schemes. These schemes are based on the assessment of two parameters: number of nuclei positive and their staining intensity. Available computerized systems define their own grading schemes based on automated measurements of low-level features, such as optical density, texture, area, and others. However, the findings produced by these systems may not be readily comprehensible by the majority of medical experts who have been accustomed to manual assessment schemes. Moreover, findings from one system can not be directly compared to findings obtained from other computerized systems. To date, no standardized assessment scheme exists for computerized systems, while interobserver and intraobserver variabilities limit the utility of the routinely used manual assessment schemes. In this paper a new system for computer-aided biopsy analysis is introduced. Here, we focus on the system's nuclear classification module. The input to this module consists of a set of six local and global features: optical density, two chromaticity indices, a variance based texture measure, global nuclei density mean, and variance. The output of the nuclei classification module consists of a membership label in a zero to four grading scheme for each detected nucleus. The classification module is based on a feedforward neural network trained in a supervised fashion to classify the nuclear feature vectors. The sample data comprises 3015 nuclei from 28 images that were classified by a human expert. A Sammon plot visualization of the six dimensional input feature space shows that the classification problem is quite difficult. The neural network used in the classification module achieved 72 % accuracy. Our results indicate that by using a nuclear classification module such as the one introduced in this paper it is possible to translate low level system measurements into a vocabulary that is familiar to medical experts. Thus, a contribution is made to the standardization of grading schemes in addition to improving the accuracy in grading breast cancer nuclei.

Analysis of Nuclei in Histopathological Sections with a System that Closely Simulates Human Experts he c v a l u a l i o n of immunocylo-cytochemistry allow the direct detection T chemically slaiimd histopathological of receptors in rnulinely processed sections prcscnts a ctiinplcx problcm due histological seclions of tumor lissiie L21. to many variations that arc inhercol i n thc In such preparations, however, the rcsults incthodology. In this rcspccl, many asare suhjcciivc and at hcsl caii only hc pccts [if iinmunocylochcinistry remain seini-quantitative; thus, immunocytounresolved, despite the l'acl lhat results cheiniciil metliuds are dis;idvantageous a s may carry imporkin1 diiigiiostic, progiios-compared to biochemical methods, which tic, and therapeutic information. In this arrender a quairtilative result in units of rc-Lick, a iiiodular neural iictwork-bascd ccplors per weight of tissue [3,4]. Dcspite approach to the dctcclion and classilica-this drawback, iminonocytocheinic~il tion of lhrcast cancer iniiclci stained for stc-incthods havegaincd wide acceptance beroid receptors i n histopathological ciiiise thcy are lcss costly, easicr Lo pcrsections is described and cvaluated. Tlic form, need sinall amounts of tissue, and system, nainetl biopsy aiialysis suppor~ inosl iinportantly, can be carried out on system (DASS), was designcd s o that it routiiic histological seclioiis. In this resimulalcs closely the a incnt procc-spcct, irninuiiocytochcinistry allows the siiiiiiltiiiicous iissessinent of tunor morphology and liormonal staining Lo be pcrh m c d 011 serial sections. In addition, this lcchnique lhiis revealctl Ihe existence of practiced by histopalhologists.

The Biopsy Analysis Support System (BASS), previously used for image analysis of immunohistochemically stained sections of breast carcinoma, has been extended to include indexing and content-based retrieval of biopsy slide images from a database of 57 captured cases. Images from histopathological biopsy slides are described and these are accessed in terms of the properties of either individual nuclei or groups of cell nuclei present in the slide. Visual similarity of cases is specified in terms of a diagnostic index, commonly known as the H-score, which incorporates the heterogeneity of nuclear staining intensity, as well as the percentage of nuclei staining at specific intensities. The system provides a platform that can be exploited in telepathology and teleconsultation, but further research is needed to explore its full potential and accuracy in a diagnostic clinical environment.

Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks trained with the backpropagation, the Kohonen's self-organizing feature maps algorithm, and the genetics based machine learning (GBML) in classifying EMG data has recently been demonstrated. A hybrid diagnostic system was also introduced that combines the above neural network and GBML models. In this paper the WISARD net is applied on the same set of EMG data. The WISARD (WiUcie, Stonham, Aleksander Recognition Device) is an implementation in hardware or software of an n-tuple sampling technique. Results suggest that although the diagnostic performance of the WISARD models is of the order of 80%, that being comparable to the above mentioned three systems, training time has been significantly reduced. In addition, the hardware or software implementation of the WISARD net is simpler than the other three systems. 'Grid size/initial gain 3Classitier size/no. of classfiers/lifetax/period of introducing the genetic algonthm/probability of crossover/probabiUy of mutation