PRISMΔDB
SAE MODEL #2

FEATURE 374

/ Medical Imaging and Oncology
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This page shows the detailed analysis of a specific feature in the Sparse Autoencoder (SAE). It includes the semantic interpretation generated by the LLM, the top activating documents that trigger this feature, and statistical metrics like density and activation distribution.

Semantic Interpretation
gemma3:12b #8

The neuron strongly activates in contexts related to medical imaging (CT, MRI), particularly in the diagnosis and treatment of cancers. This includes descriptions of imaging techniques, tumor characteristics, staging, and treatment strategies. The focus is on specific cancers (pancreatic, breast, gastric, laryngeal, intracranial) and often involves discussions of surgical resectability, vascular involvement, and treatment response. The presence of terms like 'adenocarcinoma', 'carcinoma', 'metastases', 'tumor', 'angiogenesis', 'resectable', 'CT', 'MRI', 'lymph node', and descriptions of imaging findings (e.g., 'dilated gastrocolic trunk', 'bone destruction') are key indicators. The negative examples, dealing with topics like protein sampling, teat dips, and obsessive-compulsive disorder, lack this medical imaging and oncology focus.

STATISTICS & DISTRIBUTION
Statistics Explained

Density
Fraction of documents where this feature activates at least once.
Higher density = feature appears frequently across the dataset.

Peak Activation
Maximum activation value observed for this feature over all documents.

Activation Histogram
Distribution of all activation values for this feature. Each bar represents a bin (range) of values, and its height shows how many documents fall in that range.

Density
0.02900
Peak Act
3.72
0.0 Max
Global Context
TOP ACTIVATING CONTEXTS
DOC #300 ANALYZE
ACT: 3.7245
Adenocarcinoma of the head of the pancreas: determination of surgical unresectability with thin-section pancreatic-phase helical CT. This study was conducted to evaluate newly introduced criteria for unresectability of pancreatic cancer with thin-section pancreatic-phase helical CT. Twenty-five patients with adenocarcinoma in the head of the pancr…
DOC #550 ANALYZE
ACT: 2.9631
Alterations in vascular gene expression in invasive breast carcinoma. The molecular signature that defines tumor microvasculature will likely provide clues as to how vascular-dependent tumor proliferation is regulated. Using purified endothelial cells, we generated a database of gene expression changes accompanying vascular proliferation in invasi…
DOC #94 ANALYZE
ACT: 2.9483
CT of intracranial metastases with skull and scalp involvement. Twenty-eight persons with contiguous intracranial skull, and often extracranial metastatic disease are reported. These lesions comprised 7.6% of a series of 250 consecutive patients with intracranial metastatic disease. Only three of 28 patients had other intracranial lesions and only…
DOC #54 ANALYZE
ACT: 2.6670
Pre-therapeutic evaluation of laryngeal carcinomas using computed tomography and magnetic resonance imaging. A prospective and comparative computed tomography (CT)/magnetic resonance imaging (MRI) study on 90 patients with endoscopically examined and histologically proven laryngeal malignancy is presented. Post-operative pathological and intra-rad…
DOC #289 ANALYZE
ACT: 2.5341
Skull Base Chondrosarcoma Caused by Ollier Disease: A Case Report and Literature Review. Ollier disease (OD) is a rare, nonhereditary bone disease that is characterized by the presence of multiple enchondromatosis (3 or more) with a typical asymmetric distribution which is mainly confined to the appendicular skeleton. OD's most serious complicatio…
DOC #721 ANALYZE
ACT: 2.3278
[A Case of an Elderly Patient with Advanced Gastric Cancer Successfully Treated with Combination S-1 and Oxaliplatin Therapy]. The patient was an 80-year-old man. He had a chief complaint of epigastric pain. The upper gastrointestinal endoscopy showed a type 4 tumor of the stomach, and the CT scan showed multiple para-aortic lymph node metastases.…
TOPOLOGY
CORRELATIONS
W – Weight-space · similarity between decoder vectors (features that point in similar directions in the embedding space).
D – Data / co-activation · features that tend to fire together on the same documents (co-occurrence in the dataset).