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diffuse process with uniform cytology EIN lesions start from a single cell or gland, showing loss of PTEN expression in model studies, and are therefore commonly focal lesions with volume percent stroma 50% and cytology dissimilar to the background non-neoplastic endometrium Mimics such as polyps or basalis need to be excluded The main diagnostic feature in EIN is glandular crowding, with gland/stroma ratio 50% Atypia in this system is not de ned relative to an absolute standard, but relative to the benign nonneoplastic glands in the same specimen (Fig 51) Approximately 30% of cases with EIN will progress to adenocarcinoma The management of patients with EIN lesions follows the same guidelines long established for complex atypical hyperplasia EIN is not to be confused with endometrial intraepithelial carcinoma (EIC), the precursor lesion for serous carcinoma Biopsy sampling errors have been well documented in patients with complex atypical hyperplasia/EIN undergoing hysterectomy.

Approximately 25% to 40% of these patients will be upgraded to endometrioid adenocarcinoma following thorough sampling of the endometrium from the hysterectomy specimen25 Frozen section evaluation, although imperfect, can help identify the few patients with adenocarcinoma showing signi cant myometrial invasion such that staging lymph node dissection should be performed Simple gross inspection is accurate in most cases in determining the presence or absence of signi cant myometrial invasion, but frozen section is preferred to identify the rare cases with subtle myometrial invasion that may not be identi ed on gross exam Endometrial intraepithelial carcinoma (EIC) is occasionally detected in endometrial biopsies for bleeding, usually in postmenopausal women, and should clearly be distinguished from regular endometrial hyperplasia EIC is the precursor of serous carcinoma of the endometrium.

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The program DCMPVI has been used to study the dependence of the crosstalk among the interconnections in the con gurations shown in Figs. 4.4.2a c. One of the parameters is varied in a speci c range while the others are kept xed at their typical values, chosen to be as follows: interconnection length 1000 mm, interconnection width W 1 mm, interconnection separation S 1 mm, interconnection metal resistivity r 3 m cm, interlevel distances T12 and T23 are 2 mm, GaAs substrate thickness T 200 mm, driving transistor output resistance or

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EIC is characterized by high-grade malignant cells, often scant to moderate in volume when present in biopsy specimen, seen in a background of atrophy (Plate 51) Immunohistochemical staining.

STEP 4: DRAW OUT AND INTENSIFY THE ASSOCIATED EMOTIONS The goal here is to shift the person s awareness and focus from the external circumstances to the internal reaction to them. Having begun this process in the previous step, each individual is now asked to further describe his or her reactions. In the process of description, the speakers elaborate on their internal processes. The therapist responds empathically, underlining the emotions or attitudes hinted at in the speaker s statement and asking questions to help the speaker continue to elaborate, subtly directing attention to his or her inner experience. As this process continues, the focus shifts from the external difficulty to the speaker s reaction to it. For example: The patient begins with an external focus: PATIENT: This endless treadmill of doctor s appointments is running us into the ground. Therapist shifts the focus to the patient s inner experience: THERAPIST: It is a lot! What s happening to you in the midst of all that PATIENT: I m exhausted with all this running around. Therapist helps patient to elaborate the inner experience: THERAPIST: What emotions do you find coming up in you as your exhaustion grows PATIENT: I feel I have no life left, I feel so discouraged, there s all these demands that I can t manage, I feel incompetent and angry,

For example, consider the text, nine sentences in length (Figure 2.19). Then we can rank the sentences in terms of the number of resemblances , i.e. links that they have with each other:

Viola and Jones [27] also described the option of designing a cascaded AdaBoost. For example, instead of one AdaBoost machine with 100 classi ers, one could design 10 such machines with 10 classi ers in each. In terms of precision, there will not be much difference, but testing for most images will be faster [27]. One particular image is rst tested on the rst classi er. If declared as nonsimilar, it is not tested further. If it cannot be rejected, then it is tested with the second machine. This process continues until either one machine rejects an image, or all machines approve it, and similarity is con rmed. Figure 11.4 illustrates this process. Each classi er seen in Figure 11.4 comprises one or more features. The features that de ne a classi er are chosen so that their combination eliminates as much as possible all negative images that are

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